Overview

Brought to you by YData

Dataset statistics

Number of variables55
Number of observations37104
Missing cells85089
Missing cells (%)4.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory41.5 MiB
Average record size in memory1.1 KiB

Variable types

Categorical25
Numeric26
Text4

Alerts

ID_SAEB has constant value "2023" Constant
ID_SERIE has constant value "2" Constant
IN_AMOSTRA has constant value "1" Constant
CO_CONCEITO_COESAO is highly overall correlated with CO_CONCEITO_PONTUACAO and 13 other fieldsHigh correlation
CO_CONCEITO_PONTUACAO is highly overall correlated with CO_CONCEITO_COESAO and 13 other fieldsHigh correlation
CO_CONCEITO_Q1_LP is highly overall correlated with CO_CONCEITO_COESAO and 13 other fieldsHigh correlation
CO_CONCEITO_Q1_MT is highly overall correlated with CO_RESPOSTA_TEXTO and 6 other fieldsHigh correlation
CO_CONCEITO_Q2_LP is highly overall correlated with CO_CONCEITO_COESAO and 13 other fieldsHigh correlation
CO_CONCEITO_Q2_MT is highly overall correlated with CO_RESPOSTA_TEXTO and 6 other fieldsHigh correlation
CO_CONCEITO_SEGMENTACAO is highly overall correlated with CO_CONCEITO_COESAO and 13 other fieldsHigh correlation
CO_CONCEITO_SEQUENCIA is highly overall correlated with CO_CONCEITO_COESAO and 13 other fieldsHigh correlation
CO_RESPOSTA_TEXTO is highly overall correlated with CO_CONCEITO_COESAO and 17 other fieldsHigh correlation
CO_TEXTO_GRAFIA is highly overall correlated with CO_CONCEITO_COESAO and 13 other fieldsHigh correlation
ERRO_PADRAO_LP is highly overall correlated with ERRO_PADRAO_LP_SAEB and 3 other fieldsHigh correlation
ERRO_PADRAO_LP_SAEB is highly overall correlated with ERRO_PADRAO_LP and 3 other fieldsHigh correlation
ERRO_PADRAO_MT is highly overall correlated with ERRO_PADRAO_MT_SAEB and 3 other fieldsHigh correlation
ERRO_PADRAO_MT_SAEB is highly overall correlated with ERRO_PADRAO_MT and 3 other fieldsHigh correlation
ESTRATO is highly overall correlated with ID_ALUNO and 3 other fieldsHigh correlation
ID_ALUNO is highly overall correlated with ESTRATO and 3 other fieldsHigh correlation
ID_BLOCO_1_LP is highly overall correlated with ID_BLOCO_1_MT and 2 other fieldsHigh correlation
ID_BLOCO_1_MT is highly overall correlated with ID_BLOCO_1_LP and 2 other fieldsHigh correlation
ID_BLOCO_2_LP is highly overall correlated with ID_BLOCO_2_MT and 2 other fieldsHigh correlation
ID_BLOCO_2_MT is highly overall correlated with ID_BLOCO_2_LP and 2 other fieldsHigh correlation
ID_CADERNO_LP is highly overall correlated with ID_CADERNO_MTHigh correlation
ID_CADERNO_MT is highly overall correlated with ID_CADERNO_LPHigh correlation
ID_MUNICIPIO is highly overall correlated with ESTRATO and 3 other fieldsHigh correlation
ID_REGIAO is highly overall correlated with ESTRATO and 3 other fieldsHigh correlation
ID_UF is highly overall correlated with ESTRATO and 3 other fieldsHigh correlation
IN_ALFABETIZADO is highly overall correlated with CO_CONCEITO_COESAO and 11 other fieldsHigh correlation
IN_PREENCHIMENTO_LP is highly overall correlated with CO_CONCEITO_COESAO and 19 other fieldsHigh correlation
IN_PREENCHIMENTO_MT is highly overall correlated with CO_CONCEITO_COESAO and 19 other fieldsHigh correlation
IN_PRESENCA_LP is highly overall correlated with CO_CONCEITO_COESAO and 19 other fieldsHigh correlation
IN_PRESENCA_MT is highly overall correlated with CO_CONCEITO_COESAO and 19 other fieldsHigh correlation
IN_PROFICIENCIA_LP is highly overall correlated with CO_CONCEITO_COESAO and 19 other fieldsHigh correlation
IN_PROFICIENCIA_MT is highly overall correlated with CO_CONCEITO_COESAO and 19 other fieldsHigh correlation
IN_SITUACAO_CENSO is highly overall correlated with PESO_ALUNO_LP and 1 other fieldsHigh correlation
NU_BLOCO_1_ABERTA_LP is highly overall correlated with ID_BLOCO_1_LP and 2 other fieldsHigh correlation
NU_BLOCO_1_ABERTA_MT is highly overall correlated with ID_BLOCO_1_LP and 2 other fieldsHigh correlation
NU_BLOCO_2_ABERTA_LP is highly overall correlated with ID_BLOCO_2_LP and 2 other fieldsHigh correlation
NU_BLOCO_2_ABERTA_MT is highly overall correlated with ID_BLOCO_2_LP and 2 other fieldsHigh correlation
PESO_ALUNO_LP is highly overall correlated with IN_PREENCHIMENTO_LP and 4 other fieldsHigh correlation
PESO_ALUNO_MT is highly overall correlated with IN_PREENCHIMENTO_MT and 4 other fieldsHigh correlation
PROFICIENCIA_LP is highly overall correlated with CO_RESPOSTA_TEXTO and 7 other fieldsHigh correlation
PROFICIENCIA_LP_SAEB is highly overall correlated with CO_RESPOSTA_TEXTO and 7 other fieldsHigh correlation
PROFICIENCIA_MT is highly overall correlated with IN_ALFABETIZADO and 6 other fieldsHigh correlation
PROFICIENCIA_MT_SAEB is highly overall correlated with IN_ALFABETIZADO and 6 other fieldsHigh correlation
IN_SITUACAO_CENSO is highly imbalanced (97.0%) Imbalance
PESO_ALUNO_LP has 8600 (23.2%) missing values Missing
PROFICIENCIA_LP has 8542 (23.0%) missing values Missing
ERRO_PADRAO_LP has 8542 (23.0%) missing values Missing
PROFICIENCIA_LP_SAEB has 8542 (23.0%) missing values Missing
ERRO_PADRAO_LP_SAEB has 8542 (23.0%) missing values Missing
PESO_ALUNO_MT has 8513 (22.9%) missing values Missing
PROFICIENCIA_MT has 8452 (22.8%) missing values Missing
ERRO_PADRAO_MT has 8452 (22.8%) missing values Missing
PROFICIENCIA_MT_SAEB has 8452 (22.8%) missing values Missing
ERRO_PADRAO_MT_SAEB has 8452 (22.8%) missing values Missing
ID_ALUNO has unique values Unique

Reproduction

Analysis started2025-05-20 12:26:40.335883
Analysis finished2025-05-20 12:27:05.951660
Duration25.62 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

ID_SAEB
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
2023
37104 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters148416
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023
2nd row2023
3rd row2023
4th row2023
5th row2023

Common Values

ValueCountFrequency (%)
2023 37104
100.0%

Length

2025-05-20T09:27:05.970387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-20T09:27:05.991422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2023 37104
100.0%

Most occurring characters

ValueCountFrequency (%)
2 74208
50.0%
0 37104
25.0%
3 37104
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 148416
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 74208
50.0%
0 37104
25.0%
3 37104
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 148416
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 74208
50.0%
0 37104
25.0%
3 37104
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 148416
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 74208
50.0%
0 37104
25.0%
3 37104
25.0%

ID_REGIAO
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
1
12148 
2
11549 
5
5040 
3
4896 
4
3471 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37104
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 12148
32.7%
2 11549
31.1%
5 5040
13.6%
3 4896
13.2%
4 3471
 
9.4%

Length

2025-05-20T09:27:06.010771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-20T09:27:06.030984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 12148
32.7%
2 11549
31.1%
5 5040
13.6%
3 4896
13.2%
4 3471
 
9.4%

Most occurring characters

ValueCountFrequency (%)
1 12148
32.7%
2 11549
31.1%
5 5040
13.6%
3 4896
13.2%
4 3471
 
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 12148
32.7%
2 11549
31.1%
5 5040
13.6%
3 4896
13.2%
4 3471
 
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 12148
32.7%
2 11549
31.1%
5 5040
13.6%
3 4896
13.2%
4 3471
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 12148
32.7%
2 11549
31.1%
5 5040
13.6%
3 4896
13.2%
4 3471
 
9.4%

ID_UF
Real number (ℝ)

High correlation 

Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.636373
Minimum11
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size290.0 KiB
2025-05-20T09:27:06.055400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile12
Q116
median25
Q335
95-th percentile52
Maximum53
Range42
Interquartile range (IQR)19

Descriptive statistics

Standard deviation12.912139
Coefficient of variation (CV)0.46721539
Kurtosis-0.79237414
Mean27.636373
Median Absolute Deviation (MAD)9
Skewness0.5866504
Sum1025420
Variance166.72334
MonotonicityIncreasing
2025-05-20T09:27:06.084739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
16 2528
 
6.8%
12 2112
 
5.7%
14 1873
 
5.0%
28 1580
 
4.3%
15 1565
 
4.2%
11 1440
 
3.9%
25 1421
 
3.8%
52 1383
 
3.7%
13 1365
 
3.7%
35 1346
 
3.6%
Other values (17) 20491
55.2%
ValueCountFrequency (%)
11 1440
3.9%
12 2112
5.7%
13 1365
3.7%
14 1873
5.0%
15 1565
4.2%
16 2528
6.8%
17 1265
3.4%
21 1310
3.5%
22 1265
3.4%
23 1197
3.2%
ValueCountFrequency (%)
53 1112
3.0%
52 1383
3.7%
51 1305
3.5%
50 1240
3.3%
43 1237
3.3%
42 1253
3.4%
41 981
2.6%
35 1346
3.6%
33 1249
3.4%
32 1233
3.3%

ID_MUNICIPIO
Real number (ℝ)

High correlation 

Distinct535
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6324210.2
Minimum6322171
Maximum6327738
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size290.0 KiB
2025-05-20T09:27:06.115699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6322171
5-th percentile6322229
Q16322473
median6323622
Q36325983
95-th percentile6327586
Maximum6327738
Range5567
Interquartile range (IQR)3510

Descriptive statistics

Standard deviation1902.4949
Coefficient of variation (CV)0.00030082727
Kurtosis-1.0950389
Mean6324210.2
Median Absolute Deviation (MAD)1314
Skewness0.62036301
Sum2.3465349 × 1011
Variance3619486.8
MonotonicityIncreasing
2025-05-20T09:27:06.149453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6322473 1462
 
3.9%
6322308 1203
 
3.2%
6327738 1112
 
3.0%
6322236 1036
 
2.8%
6322281 671
 
1.8%
6322186 635
 
1.7%
6323052 626
 
1.7%
6322339 593
 
1.6%
6322805 584
 
1.6%
6323866 551
 
1.5%
Other values (525) 28631
77.2%
ValueCountFrequency (%)
6322171 107
 
0.3%
6322173 27
 
0.1%
6322179 69
 
0.2%
6322181 214
 
0.6%
6322182 19
 
0.1%
6322186 635
1.7%
6322189 50
 
0.1%
6322191 80
 
0.2%
6322193 22
 
0.1%
6322196 57
 
0.2%
ValueCountFrequency (%)
6327738 1112
3.0%
6327732 180
 
0.5%
6327729 58
 
0.2%
6327689 61
 
0.2%
6327680 24
 
0.1%
6327665 35
 
0.1%
6327634 19
 
0.1%
6327626 15
 
< 0.1%
6327610 23
 
0.1%
6327602 19
 
0.1%

ID_AREA
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
2
21372 
1
15732 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37104
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 21372
57.6%
1 15732
42.4%

Length

2025-05-20T09:27:06.179701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-20T09:27:06.196397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 21372
57.6%
1 15732
42.4%

Most occurring characters

ValueCountFrequency (%)
2 21372
57.6%
1 15732
42.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 21372
57.6%
1 15732
42.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 21372
57.6%
1 15732
42.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 21372
57.6%
1 15732
42.4%

ID_ESCOLA
Real number (ℝ)

Distinct1181
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61436560
Minimum61398250
Maximum61471778
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size290.0 KiB
2025-05-20T09:27:06.220189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum61398250
5-th percentile61403285
Q161419709
median61437224
Q361455512
95-th percentile61469128
Maximum61471778
Range73528
Interquartile range (IQR)35803

Descriptive statistics

Standard deviation21458.691
Coefficient of variation (CV)0.0003492821
Kurtosis-1.2247616
Mean61436560
Median Absolute Deviation (MAD)17860
Skewness-0.037842739
Sum2.2795421 × 1012
Variance4.6047541 × 108
MonotonicityNot monotonic
2025-05-20T09:27:06.255946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61438412 80
 
0.2%
61427800 78
 
0.2%
61470406 77
 
0.2%
61421585 77
 
0.2%
61421759 76
 
0.2%
61456246 74
 
0.2%
61404851 71
 
0.2%
61454767 71
 
0.2%
61404836 70
 
0.2%
61447028 70
 
0.2%
Other values (1171) 36360
98.0%
ValueCountFrequency (%)
61398250 22
0.1%
61398260 21
0.1%
61398287 37
0.1%
61398414 22
0.1%
61398419 25
0.1%
61398442 47
0.1%
61398453 26
0.1%
61398659 15
 
< 0.1%
61398672 16
 
< 0.1%
61398887 17
 
< 0.1%
ValueCountFrequency (%)
61471778 27
0.1%
61471568 20
 
0.1%
61471566 15
 
< 0.1%
61471539 61
0.2%
61471528 17
 
< 0.1%
61471355 12
 
< 0.1%
61471287 21
 
0.1%
61471086 24
 
0.1%
61471076 28
0.1%
61471072 16
 
< 0.1%

IN_PUBLICA
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
1
25863 
0
11241 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37104
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 25863
69.7%
0 11241
30.3%

Length

2025-05-20T09:27:06.287965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-20T09:27:06.303967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 25863
69.7%
0 11241
30.3%

Most occurring characters

ValueCountFrequency (%)
1 25863
69.7%
0 11241
30.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 25863
69.7%
0 11241
30.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 25863
69.7%
0 11241
30.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 25863
69.7%
0 11241
30.3%

ID_LOCALIZACAO
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
1
28899 
2
8205 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37104
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 28899
77.9%
2 8205
 
22.1%

Length

2025-05-20T09:27:06.325038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-20T09:27:06.341059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 28899
77.9%
2 8205
 
22.1%

Most occurring characters

ValueCountFrequency (%)
1 28899
77.9%
2 8205
 
22.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 28899
77.9%
2 8205
 
22.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 28899
77.9%
2 8205
 
22.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 28899
77.9%
2 8205
 
22.1%

ID_TURMA
Real number (ℝ)

Distinct1612
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1600874.3
Minimum1475329
Maximum1727588
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size290.0 KiB
2025-05-20T09:27:06.364336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1475329
5-th percentile1490398
Q11534341
median1602075.5
Q31664959
95-th percentile1714219
Maximum1727588
Range252259
Interquartile range (IQR)130618

Descriptive statistics

Standard deviation72943.369
Coefficient of variation (CV)0.045564709
Kurtosis-1.2112769
Mean1600874.3
Median Absolute Deviation (MAD)63339.5
Skewness0.0064229228
Sum5.9398838 × 1010
Variance5.3207351 × 109
MonotonicityNot monotonic
2025-05-20T09:27:06.397665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1658447 51
 
0.1%
1676003 50
 
0.1%
1579581 47
 
0.1%
1620200 42
 
0.1%
1571117 40
 
0.1%
1515508 40
 
0.1%
1682437 39
 
0.1%
1601960 39
 
0.1%
1688427 39
 
0.1%
1484043 39
 
0.1%
Other values (1602) 36678
98.9%
ValueCountFrequency (%)
1475329 26
0.1%
1475331 28
0.1%
1475333 18
< 0.1%
1475367 10
 
< 0.1%
1475425 17
< 0.1%
1475432 26
0.1%
1475464 39
0.1%
1475619 17
< 0.1%
1475663 16
< 0.1%
1475672 13
 
< 0.1%
ValueCountFrequency (%)
1727588 11
 
< 0.1%
1727579 12
 
< 0.1%
1727495 33
0.1%
1727324 14
< 0.1%
1727199 17
< 0.1%
1727117 26
0.1%
1727113 32
0.1%
1726922 25
0.1%
1726688 27
0.1%
1726653 25
0.1%

ID_SERIE
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
2
37104 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37104
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 37104
100.0%

Length

2025-05-20T09:27:06.428440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-20T09:27:06.442946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 37104
100.0%

Most occurring characters

ValueCountFrequency (%)
2 37104
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 37104
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 37104
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 37104
100.0%

ID_ALUNO
Real number (ℝ)

High correlation  Unique 

Distinct37104
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52030857
Minimum49372737
Maximum56443913
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size290.0 KiB
2025-05-20T09:27:06.464603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum49372737
5-th percentile49454546
Q150060853
median51134200
Q354172959
95-th percentile56239225
Maximum56443913
Range7071176
Interquartile range (IQR)4112106.5

Descriptive statistics

Standard deviation2388629.7
Coefficient of variation (CV)0.045907944
Kurtosis-1.0462077
Mean52030857
Median Absolute Deviation (MAD)1440621
Skewness0.67966156
Sum1.9305529 × 1012
Variance5.7055517 × 1012
MonotonicityNot monotonic
2025-05-20T09:27:06.498247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49392472 1
 
< 0.1%
52923837 1
 
< 0.1%
52546454 1
 
< 0.1%
52210591 1
 
< 0.1%
52210592 1
 
< 0.1%
52210593 1
 
< 0.1%
52210594 1
 
< 0.1%
52210595 1
 
< 0.1%
52214733 1
 
< 0.1%
52214734 1
 
< 0.1%
Other values (37094) 37094
> 99.9%
ValueCountFrequency (%)
49372737 1
< 0.1%
49372738 1
< 0.1%
49372739 1
< 0.1%
49372740 1
< 0.1%
49372741 1
< 0.1%
49372742 1
< 0.1%
49372743 1
< 0.1%
49372744 1
< 0.1%
49372745 1
< 0.1%
49372746 1
< 0.1%
ValueCountFrequency (%)
56443913 1
< 0.1%
56443912 1
< 0.1%
56443911 1
< 0.1%
56442545 1
< 0.1%
56442544 1
< 0.1%
56439373 1
< 0.1%
56439372 1
< 0.1%
56439371 1
< 0.1%
56438374 1
< 0.1%
56438373 1
< 0.1%

IN_SITUACAO_CENSO
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
1
36989 
0
 
115

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37104
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 36989
99.7%
0 115
 
0.3%

Length

2025-05-20T09:27:06.527464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-20T09:27:06.543334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 36989
99.7%
0 115
 
0.3%

Most occurring characters

ValueCountFrequency (%)
1 36989
99.7%
0 115
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 36989
99.7%
0 115
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 36989
99.7%
0 115
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 36989
99.7%
0 115
 
0.3%

IN_PREENCHIMENTO_LP
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
1
28578 
0
8526 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37104
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 28578
77.0%
0 8526
 
23.0%

Length

2025-05-20T09:27:06.562777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-20T09:27:06.579601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 28578
77.0%
0 8526
 
23.0%

Most occurring characters

ValueCountFrequency (%)
1 28578
77.0%
0 8526
 
23.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 28578
77.0%
0 8526
 
23.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 28578
77.0%
0 8526
 
23.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 28578
77.0%
0 8526
 
23.0%

IN_PREENCHIMENTO_MT
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
1
28660 
0
8444 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37104
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 28660
77.2%
0 8444
 
22.8%

Length

2025-05-20T09:27:06.599870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-20T09:27:06.615838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 28660
77.2%
0 8444
 
22.8%

Most occurring characters

ValueCountFrequency (%)
1 28660
77.2%
0 8444
 
22.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 28660
77.2%
0 8444
 
22.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 28660
77.2%
0 8444
 
22.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 28660
77.2%
0 8444
 
22.8%

IN_PRESENCA_LP
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
1
28754 
0
8350 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37104
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 28754
77.5%
0 8350
 
22.5%

Length

2025-05-20T09:27:06.636580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-20T09:27:06.652207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 28754
77.5%
0 8350
 
22.5%

Most occurring characters

ValueCountFrequency (%)
1 28754
77.5%
0 8350
 
22.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 28754
77.5%
0 8350
 
22.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 28754
77.5%
0 8350
 
22.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 28754
77.5%
0 8350
 
22.5%

IN_PRESENCA_MT
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
1
28875 
0
8229 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37104
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 28875
77.8%
0 8229
 
22.2%

Length

2025-05-20T09:27:06.673297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-20T09:27:06.689018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 28875
77.8%
0 8229
 
22.2%

Most occurring characters

ValueCountFrequency (%)
1 28875
77.8%
0 8229
 
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 28875
77.8%
0 8229
 
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 28875
77.8%
0 8229
 
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 28875
77.8%
0 8229
 
22.2%

ID_CADERNO_LP
Real number (ℝ)

High correlation 

Distinct22
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.003369
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size290.0 KiB
2025-05-20T09:27:06.707618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median11
Q316
95-th percentile20
Maximum22
Range21
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.0594771
Coefficient of variation (CV)0.55069289
Kurtosis-1.205914
Mean11.003369
Median Absolute Deviation (MAD)5
Skewness-0.0015416555
Sum408269
Variance36.717262
MonotonicityNot monotonic
2025-05-20T09:27:06.732873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
19 1819
 
4.9%
10 1818
 
4.9%
11 1805
 
4.9%
18 1804
 
4.9%
7 1803
 
4.9%
1 1787
 
4.8%
15 1785
 
4.8%
6 1783
 
4.8%
16 1777
 
4.8%
3 1774
 
4.8%
Other values (12) 19149
51.6%
ValueCountFrequency (%)
1 1787
4.8%
2 1770
4.8%
3 1774
4.8%
4 1708
4.6%
5 1770
4.8%
6 1783
4.8%
7 1803
4.9%
8 1719
4.6%
9 1747
4.7%
10 1818
4.9%
ValueCountFrequency (%)
22 34
 
0.1%
21 1718
4.6%
20 1735
4.7%
19 1819
4.9%
18 1804
4.9%
17 1750
4.7%
16 1777
4.8%
15 1785
4.8%
14 1754
4.7%
13 1709
4.6%

ID_BLOCO_1_LP
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9918607
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size290.0 KiB
2025-05-20T09:27:06.754227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9982173
Coefficient of variation (CV)0.5005729
Kurtosis-1.245878
Mean3.9918607
Median Absolute Deviation (MAD)2
Skewness0.0057884388
Sum148114
Variance3.9928723
MonotonicityNot monotonic
2025-05-20T09:27:06.773564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 5342
14.4%
1 5325
14.4%
5 5324
14.3%
4 5317
14.3%
2 5294
14.3%
7 5275
14.2%
6 5227
14.1%
ValueCountFrequency (%)
1 5325
14.4%
2 5294
14.3%
3 5342
14.4%
4 5317
14.3%
5 5324
14.3%
6 5227
14.1%
7 5275
14.2%
ValueCountFrequency (%)
7 5275
14.2%
6 5227
14.1%
5 5324
14.3%
4 5317
14.3%
3 5342
14.4%
2 5294
14.3%
1 5325
14.4%

ID_BLOCO_2_LP
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0026682
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size290.0 KiB
2025-05-20T09:27:06.792149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0034653
Coefficient of variation (CV)0.50053244
Kurtosis-1.2541979
Mean4.0026682
Median Absolute Deviation (MAD)2
Skewness-0.0040860558
Sum148515
Variance4.0138732
MonotonicityNot monotonic
2025-05-20T09:27:06.811282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 5331
14.4%
6 5325
14.4%
7 5322
14.3%
2 5310
14.3%
4 5306
14.3%
5 5303
14.3%
3 5207
14.0%
ValueCountFrequency (%)
1 5331
14.4%
2 5310
14.3%
3 5207
14.0%
4 5306
14.3%
5 5303
14.3%
6 5325
14.4%
7 5322
14.3%
ValueCountFrequency (%)
7 5322
14.3%
6 5325
14.4%
5 5303
14.3%
4 5306
14.3%
3 5207
14.0%
2 5310
14.3%
1 5331
14.4%

NU_BLOCO_1_ABERTA_LP
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9946097
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size290.0 KiB
2025-05-20T09:27:06.830487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9961619
Coefficient of variation (CV)0.49971386
Kurtosis-1.2433038
Mean3.9946097
Median Absolute Deviation (MAD)2
Skewness0.0047601722
Sum148216
Variance3.9846622
MonotonicityNot monotonic
2025-05-20T09:27:06.849773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 5351
14.4%
3 5342
14.4%
5 5324
14.3%
2 5294
14.3%
1 5291
14.3%
7 5275
14.2%
6 5227
14.1%
ValueCountFrequency (%)
1 5291
14.3%
2 5294
14.3%
3 5342
14.4%
4 5351
14.4%
5 5324
14.3%
6 5227
14.1%
7 5275
14.2%
ValueCountFrequency (%)
7 5275
14.2%
6 5227
14.1%
5 5324
14.3%
4 5351
14.4%
3 5342
14.4%
2 5294
14.3%
1 5291
14.3%

NU_BLOCO_2_ABERTA_LP
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0072499
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size290.0 KiB
2025-05-20T09:27:06.867761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0045971
Coefficient of variation (CV)0.5002426
Kurtosis-1.2544375
Mean4.0072499
Median Absolute Deviation (MAD)2
Skewness-0.0069590693
Sum148685
Variance4.0184096
MonotonicityNot monotonic
2025-05-20T09:27:06.887661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7 5356
14.4%
1 5331
14.4%
6 5325
14.4%
4 5306
14.3%
5 5303
14.3%
2 5276
14.2%
3 5207
14.0%
ValueCountFrequency (%)
1 5331
14.4%
2 5276
14.2%
3 5207
14.0%
4 5306
14.3%
5 5303
14.3%
6 5325
14.4%
7 5356
14.4%
ValueCountFrequency (%)
7 5356
14.4%
6 5325
14.4%
5 5303
14.3%
4 5306
14.3%
3 5207
14.0%
2 5276
14.2%
1 5331
14.4%

ID_CADERNO_MT
Real number (ℝ)

High correlation 

Distinct22
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.003369
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size290.0 KiB
2025-05-20T09:27:06.913579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median11
Q316
95-th percentile20
Maximum22
Range21
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.0594771
Coefficient of variation (CV)0.55069289
Kurtosis-1.205914
Mean11.003369
Median Absolute Deviation (MAD)5
Skewness-0.0015416555
Sum408269
Variance36.717262
MonotonicityNot monotonic
2025-05-20T09:27:06.940999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
19 1819
 
4.9%
10 1818
 
4.9%
11 1805
 
4.9%
18 1804
 
4.9%
7 1803
 
4.9%
1 1787
 
4.8%
15 1785
 
4.8%
6 1783
 
4.8%
16 1777
 
4.8%
3 1774
 
4.8%
Other values (12) 19149
51.6%
ValueCountFrequency (%)
1 1787
4.8%
2 1770
4.8%
3 1774
4.8%
4 1708
4.6%
5 1770
4.8%
6 1783
4.8%
7 1803
4.9%
8 1719
4.6%
9 1747
4.7%
10 1818
4.9%
ValueCountFrequency (%)
22 34
 
0.1%
21 1718
4.6%
20 1735
4.7%
19 1819
4.9%
18 1804
4.9%
17 1750
4.7%
16 1777
4.8%
15 1785
4.8%
14 1754
4.7%
13 1709
4.6%

ID_BLOCO_1_MT
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9918607
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size290.0 KiB
2025-05-20T09:27:06.964751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9982173
Coefficient of variation (CV)0.5005729
Kurtosis-1.245878
Mean3.9918607
Median Absolute Deviation (MAD)2
Skewness0.0057884388
Sum148114
Variance3.9928723
MonotonicityNot monotonic
2025-05-20T09:27:06.986690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 5342
14.4%
1 5325
14.4%
5 5324
14.3%
4 5317
14.3%
2 5294
14.3%
7 5275
14.2%
6 5227
14.1%
ValueCountFrequency (%)
1 5325
14.4%
2 5294
14.3%
3 5342
14.4%
4 5317
14.3%
5 5324
14.3%
6 5227
14.1%
7 5275
14.2%
ValueCountFrequency (%)
7 5275
14.2%
6 5227
14.1%
5 5324
14.3%
4 5317
14.3%
3 5342
14.4%
2 5294
14.3%
1 5325
14.4%

ID_BLOCO_2_MT
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0026682
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size290.0 KiB
2025-05-20T09:27:07.005831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0034653
Coefficient of variation (CV)0.50053244
Kurtosis-1.2541979
Mean4.0026682
Median Absolute Deviation (MAD)2
Skewness-0.0040860558
Sum148515
Variance4.0138732
MonotonicityNot monotonic
2025-05-20T09:27:07.026463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 5331
14.4%
6 5325
14.4%
7 5322
14.3%
2 5310
14.3%
4 5306
14.3%
5 5303
14.3%
3 5207
14.0%
ValueCountFrequency (%)
1 5331
14.4%
2 5310
14.3%
3 5207
14.0%
4 5306
14.3%
5 5303
14.3%
6 5325
14.4%
7 5322
14.3%
ValueCountFrequency (%)
7 5322
14.3%
6 5325
14.4%
5 5303
14.3%
4 5306
14.3%
3 5207
14.0%
2 5310
14.3%
1 5331
14.4%

NU_BLOCO_1_ABERTA_MT
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9918607
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size290.0 KiB
2025-05-20T09:27:07.044789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9982173
Coefficient of variation (CV)0.5005729
Kurtosis-1.245878
Mean3.9918607
Median Absolute Deviation (MAD)2
Skewness0.0057884388
Sum148114
Variance3.9928723
MonotonicityNot monotonic
2025-05-20T09:27:07.064759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 5342
14.4%
1 5325
14.4%
5 5324
14.3%
4 5317
14.3%
2 5294
14.3%
7 5275
14.2%
6 5227
14.1%
ValueCountFrequency (%)
1 5325
14.4%
2 5294
14.3%
3 5342
14.4%
4 5317
14.3%
5 5324
14.3%
6 5227
14.1%
7 5275
14.2%
ValueCountFrequency (%)
7 5275
14.2%
6 5227
14.1%
5 5324
14.3%
4 5317
14.3%
3 5342
14.4%
2 5294
14.3%
1 5325
14.4%

NU_BLOCO_2_ABERTA_MT
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0026682
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size290.0 KiB
2025-05-20T09:27:07.083467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0034653
Coefficient of variation (CV)0.50053244
Kurtosis-1.2541979
Mean4.0026682
Median Absolute Deviation (MAD)2
Skewness-0.0040860558
Sum148515
Variance4.0138732
MonotonicityNot monotonic
2025-05-20T09:27:07.105388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 5331
14.4%
6 5325
14.4%
7 5322
14.3%
2 5310
14.3%
4 5306
14.3%
5 5303
14.3%
3 5207
14.0%
ValueCountFrequency (%)
1 5331
14.4%
2 5310
14.3%
3 5207
14.0%
4 5306
14.3%
5 5303
14.3%
6 5325
14.4%
7 5322
14.3%
ValueCountFrequency (%)
7 5322
14.3%
6 5325
14.4%
5 5303
14.3%
4 5306
14.3%
3 5207
14.0%
2 5310
14.3%
1 5331
14.4%
Distinct10304
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
2025-05-20T09:27:07.180272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters296832
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7386 ?
Unique (%)19.9%

Sample

1st rowDCBBBBBA
2nd rowDACBBCBC
3rd rowDCDBDDCA
4th rowCCACBADC
5th rowDCACBADC
ValueCountFrequency (%)
8528
 
23.0%
dcbcdaad 1067
 
2.9%
dcdadbac 636
 
1.7%
accbcbad 603
 
1.6%
ddccdcab 518
 
1.4%
dcacbadc 501
 
1.4%
adcbcdbc 384
 
1.0%
dcbcbadc 361
 
1.0%
ddacdbdc 262
 
0.7%
ddccdcbb 256
 
0.7%
Other values (10284) 23988
64.7%
2025-05-20T09:27:07.281860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 69202
23.3%
D 66559
22.4%
C 64463
21.7%
A 51899
17.5%
B 44452
15.0%
* 257
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 296832
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 69202
23.3%
D 66559
22.4%
C 64463
21.7%
A 51899
17.5%
B 44452
15.0%
* 257
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 296832
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 69202
23.3%
D 66559
22.4%
C 64463
21.7%
A 51899
17.5%
B 44452
15.0%
* 257
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 296832
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 69202
23.3%
D 66559
22.4%
C 64463
21.7%
A 51899
17.5%
B 44452
15.0%
* 257
 
0.1%
Distinct10595
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
2025-05-20T09:27:07.358658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters296832
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7544 ?
Unique (%)20.3%

Sample

1st rowDDBDACBB
2nd rowCDACCBAA
3rd rowADDDDDAD
4th rowDDDDCCBB
5th rowDDCCDCAB
ValueCountFrequency (%)
8550
 
23.0%
dcbcdaad 1007
 
2.7%
accbcbad 650
 
1.8%
dcacbadc 576
 
1.6%
dcdadbac 514
 
1.4%
ddccdcab 467
 
1.3%
adcbcdbc 453
 
1.2%
ddacdbdc 351
 
0.9%
dcbcbadc 297
 
0.8%
dcdadbaa 205
 
0.6%
Other values (10577) 24034
64.8%
2025-05-20T09:27:07.464011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 69280
23.3%
D 64643
21.8%
C 63834
21.5%
A 53199
17.9%
B 45662
15.4%
* 214
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 296832
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 69280
23.3%
D 64643
21.8%
C 63834
21.5%
A 53199
17.9%
B 45662
15.4%
* 214
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 296832
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 69280
23.3%
D 64643
21.8%
C 63834
21.5%
A 53199
17.9%
B 45662
15.4%
* 214
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 296832
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 69280
23.3%
D 64643
21.8%
C 63834
21.5%
A 53199
17.9%
B 45662
15.4%
* 214
 
0.1%

CO_CONCEITO_Q1_LP
Categorical

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
A
15136 
.
9038 
B
8234 
G
2791 
E
1752 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37104
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowE
4th rowG
5th rowA

Common Values

ValueCountFrequency (%)
A 15136
40.8%
. 9038
24.4%
B 8234
22.2%
G 2791
 
7.5%
E 1752
 
4.7%
F 153
 
0.4%

Length

2025-05-20T09:27:07.496596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-20T09:27:07.518918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
a 15136
40.8%
9038
24.4%
b 8234
22.2%
g 2791
 
7.5%
e 1752
 
4.7%
f 153
 
0.4%

Most occurring characters

ValueCountFrequency (%)
A 15136
40.8%
. 9038
24.4%
B 8234
22.2%
G 2791
 
7.5%
E 1752
 
4.7%
F 153
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 15136
40.8%
. 9038
24.4%
B 8234
22.2%
G 2791
 
7.5%
E 1752
 
4.7%
F 153
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 15136
40.8%
. 9038
24.4%
B 8234
22.2%
G 2791
 
7.5%
E 1752
 
4.7%
F 153
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 15136
40.8%
. 9038
24.4%
B 8234
22.2%
G 2791
 
7.5%
E 1752
 
4.7%
F 153
 
0.4%

CO_CONCEITO_Q2_LP
Categorical

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
A
14600 
.
9374 
B
8316 
G
2860 
E
1803 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37104
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowE
4th row.
5th rowA

Common Values

ValueCountFrequency (%)
A 14600
39.3%
. 9374
25.3%
B 8316
22.4%
G 2860
 
7.7%
E 1803
 
4.9%
F 151
 
0.4%

Length

2025-05-20T09:27:07.548815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-20T09:27:07.570673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
a 14600
39.3%
9374
25.3%
b 8316
22.4%
g 2860
 
7.7%
e 1803
 
4.9%
f 151
 
0.4%

Most occurring characters

ValueCountFrequency (%)
A 14600
39.3%
. 9374
25.3%
B 8316
22.4%
G 2860
 
7.7%
E 1803
 
4.9%
F 151
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 14600
39.3%
. 9374
25.3%
B 8316
22.4%
G 2860
 
7.7%
E 1803
 
4.9%
F 151
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 14600
39.3%
. 9374
25.3%
B 8316
22.4%
G 2860
 
7.7%
E 1803
 
4.9%
F 151
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 14600
39.3%
. 9374
25.3%
B 8316
22.4%
G 2860
 
7.7%
E 1803
 
4.9%
F 151
 
0.4%

CO_RESPOSTA_TEXTO
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
TX
20664 
BR
9747 
NL
6693 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters74208
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTX
2nd rowTX
3rd rowNL
4th rowTX
5th rowTX

Common Values

ValueCountFrequency (%)
TX 20664
55.7%
BR 9747
26.3%
NL 6693
 
18.0%

Length

2025-05-20T09:27:07.599849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-20T09:27:07.625342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
tx 20664
55.7%
br 9747
26.3%
nl 6693
 
18.0%

Most occurring characters

ValueCountFrequency (%)
T 20664
27.8%
X 20664
27.8%
B 9747
13.1%
R 9747
13.1%
N 6693
 
9.0%
L 6693
 
9.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 74208
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 20664
27.8%
X 20664
27.8%
B 9747
13.1%
R 9747
13.1%
N 6693
 
9.0%
L 6693
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 74208
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 20664
27.8%
X 20664
27.8%
B 9747
13.1%
R 9747
13.1%
N 6693
 
9.0%
L 6693
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 74208
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 20664
27.8%
X 20664
27.8%
B 9747
13.1%
R 9747
13.1%
N 6693
 
9.0%
L 6693
 
9.0%

CO_CONCEITO_SEQUENCIA
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
B
9836 
.
9747 
*
6693 
A
5723 
C
5105 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37104
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd row*
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
B 9836
26.5%
. 9747
26.3%
* 6693
18.0%
A 5723
15.4%
C 5105
13.8%

Length

2025-05-20T09:27:07.652804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-20T09:27:07.887785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
16440
44.3%
b 9836
26.5%
a 5723
 
15.4%
c 5105
 
13.8%

Most occurring characters

ValueCountFrequency (%)
B 9836
26.5%
. 9747
26.3%
* 6693
18.0%
A 5723
15.4%
C 5105
13.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 9836
26.5%
. 9747
26.3%
* 6693
18.0%
A 5723
15.4%
C 5105
13.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 9836
26.5%
. 9747
26.3%
* 6693
18.0%
A 5723
15.4%
C 5105
13.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 9836
26.5%
. 9747
26.3%
* 6693
18.0%
A 5723
15.4%
C 5105
13.8%

CO_CONCEITO_COESAO
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
A
14639 
.
9747 
*
6693 
B
4221 
C
1804 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37104
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowB
3rd row*
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 14639
39.5%
. 9747
26.3%
* 6693
18.0%
B 4221
 
11.4%
C 1804
 
4.9%

Length

2025-05-20T09:27:07.916950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-20T09:27:07.939710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
16440
44.3%
a 14639
39.5%
b 4221
 
11.4%
c 1804
 
4.9%

Most occurring characters

ValueCountFrequency (%)
A 14639
39.5%
. 9747
26.3%
* 6693
18.0%
B 4221
 
11.4%
C 1804
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 14639
39.5%
. 9747
26.3%
* 6693
18.0%
B 4221
 
11.4%
C 1804
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 14639
39.5%
. 9747
26.3%
* 6693
18.0%
B 4221
 
11.4%
C 1804
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 14639
39.5%
. 9747
26.3%
* 6693
18.0%
B 4221
 
11.4%
C 1804
 
4.9%

CO_CONCEITO_PONTUACAO
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
C
13734 
.
9747 
*
6693 
B
5200 
A
1730 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37104
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd row*
4th rowB
5th rowC

Common Values

ValueCountFrequency (%)
C 13734
37.0%
. 9747
26.3%
* 6693
18.0%
B 5200
 
14.0%
A 1730
 
4.7%

Length

2025-05-20T09:27:07.966956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-20T09:27:07.989204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
16440
44.3%
c 13734
37.0%
b 5200
 
14.0%
a 1730
 
4.7%

Most occurring characters

ValueCountFrequency (%)
C 13734
37.0%
. 9747
26.3%
* 6693
18.0%
B 5200
 
14.0%
A 1730
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 13734
37.0%
. 9747
26.3%
* 6693
18.0%
B 5200
 
14.0%
A 1730
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 13734
37.0%
. 9747
26.3%
* 6693
18.0%
B 5200
 
14.0%
A 1730
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 13734
37.0%
. 9747
26.3%
* 6693
18.0%
B 5200
 
14.0%
A 1730
 
4.7%

CO_CONCEITO_SEGMENTACAO
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
.
9747 
B
9703 
A
7646 
*
6693 
C
3315 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37104
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowB
3rd row*
4th rowB
5th rowA

Common Values

ValueCountFrequency (%)
. 9747
26.3%
B 9703
26.2%
A 7646
20.6%
* 6693
18.0%
C 3315
 
8.9%

Length

2025-05-20T09:27:08.016712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-20T09:27:08.042941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
16440
44.3%
b 9703
26.2%
a 7646
20.6%
c 3315
 
8.9%

Most occurring characters

ValueCountFrequency (%)
. 9747
26.3%
B 9703
26.2%
A 7646
20.6%
* 6693
18.0%
C 3315
 
8.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 9747
26.3%
B 9703
26.2%
A 7646
20.6%
* 6693
18.0%
C 3315
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 9747
26.3%
B 9703
26.2%
A 7646
20.6%
* 6693
18.0%
C 3315
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 9747
26.3%
B 9703
26.2%
A 7646
20.6%
* 6693
18.0%
C 3315
 
8.9%

CO_TEXTO_GRAFIA
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
A
16818 
.
9747 
*
6693 
B
3053 
C
 
793

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37104
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowA
3rd row*
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 16818
45.3%
. 9747
26.3%
* 6693
 
18.0%
B 3053
 
8.2%
C 793
 
2.1%

Length

2025-05-20T09:27:08.072666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-20T09:27:08.094792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
a 16818
45.3%
16440
44.3%
b 3053
 
8.2%
c 793
 
2.1%

Most occurring characters

ValueCountFrequency (%)
A 16818
45.3%
. 9747
26.3%
* 6693
 
18.0%
B 3053
 
8.2%
C 793
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 16818
45.3%
. 9747
26.3%
* 6693
 
18.0%
B 3053
 
8.2%
C 793
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 16818
45.3%
. 9747
26.3%
* 6693
 
18.0%
B 3053
 
8.2%
C 793
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 16818
45.3%
. 9747
26.3%
* 6693
 
18.0%
B 3053
 
8.2%
C 793
 
2.1%
Distinct11905
Distinct (%)32.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
2025-05-20T09:27:08.173861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters333936
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8761 ?
Unique (%)23.6%

Sample

1st rowABBDCCDCA
2nd rowABDBACBCB
3rd rowACBCDDCBB
4th rowABBDACCBA
5th rowABBDACCBA
ValueCountFrequency (%)
8444
 
22.8%
cbbbdcaba 685
 
1.8%
cbabacdbb 625
 
1.7%
abbdaccba 551
 
1.5%
aadcdcdac 492
 
1.3%
dacddcbba 412
 
1.1%
dddbddcac 345
 
0.9%
cbbbdcaca 238
 
0.6%
cbbbdcaaa 225
 
0.6%
badbdccca 205
 
0.6%
Other values (11890) 24882
67.1%
2025-05-20T09:27:08.286443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 76742
23.0%
C 73170
21.9%
B 67933
20.3%
A 59121
17.7%
D 56707
17.0%
* 263
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 333936
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 76742
23.0%
C 73170
21.9%
B 67933
20.3%
A 59121
17.7%
D 56707
17.0%
* 263
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 333936
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 76742
23.0%
C 73170
21.9%
B 67933
20.3%
A 59121
17.7%
D 56707
17.0%
* 263
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 333936
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 76742
23.0%
C 73170
21.9%
B 67933
20.3%
A 59121
17.7%
D 56707
17.0%
* 263
 
0.1%
Distinct12244
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
2025-05-20T09:27:08.356240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters333936
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9195 ?
Unique (%)24.8%

Sample

1st rowACDBDCDBC
2nd rowAAACDCDCC
3rd rowDDDCDCDCC
4th rowAADCDCDBC
5th rowAADCDCDAC
ValueCountFrequency (%)
8470
 
22.8%
cbbbdcaba 669
 
1.8%
abbdaccba 643
 
1.7%
cbabacdbb 642
 
1.7%
aadcdcdac 477
 
1.3%
dacddcbba 439
 
1.2%
dddbddcac 268
 
0.7%
cbbbdcaca 212
 
0.6%
cbbbdcaaa 199
 
0.5%
aadcdcdbc 167
 
0.5%
Other values (12225) 24918
67.2%
2025-05-20T09:27:08.451176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 77009
23.1%
C 72810
21.8%
B 68226
20.4%
A 59638
17.9%
D 55970
16.8%
* 283
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 333936
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 77009
23.1%
C 72810
21.8%
B 68226
20.4%
A 59638
17.9%
D 55970
16.8%
* 283
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 333936
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 77009
23.1%
C 72810
21.8%
B 68226
20.4%
A 59638
17.9%
D 55970
16.8%
* 283
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 333936
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 77009
23.1%
C 72810
21.8%
B 68226
20.4%
A 59638
17.9%
D 55970
16.8%
* 283
 
0.1%

CO_CONCEITO_Q1_MT
Categorical

High correlation 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
20
10713 
.
8813 
0
7431 
21
4204 
12
1700 
Other values (5)
4243 

Length

Max length2
Median length2
Mean length1.5622035
Min length1

Characters and Unicode

Total characters57964
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row21
2nd row21
3rd row11
4th row21
5th row21

Common Values

ValueCountFrequency (%)
20 10713
28.9%
. 8813
23.8%
0 7431
20.0%
21 4204
 
11.3%
12 1700
 
4.6%
11 1684
 
4.5%
10 1389
 
3.7%
22 880
 
2.4%
23 251
 
0.7%
13 39
 
0.1%

Length

2025-05-20T09:27:08.481202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-20T09:27:08.508305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
20 10713
28.9%
8813
23.8%
0 7431
20.0%
21 4204
 
11.3%
12 1700
 
4.6%
11 1684
 
4.5%
10 1389
 
3.7%
22 880
 
2.4%
23 251
 
0.7%
13 39
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 19533
33.7%
2 18628
32.1%
1 10700
18.5%
. 8813
15.2%
3 290
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 57964
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 19533
33.7%
2 18628
32.1%
1 10700
18.5%
. 8813
15.2%
3 290
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 57964
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 19533
33.7%
2 18628
32.1%
1 10700
18.5%
. 8813
15.2%
3 290
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 57964
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 19533
33.7%
2 18628
32.1%
1 10700
18.5%
. 8813
15.2%
3 290
 
0.5%

CO_CONCEITO_Q2_MT
Categorical

High correlation 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
20
11095 
.
8917 
0
7508 
21
4395 
12
1588 
Other values (5)
3601 

Length

Max length2
Median length2
Mean length1.5573254
Min length1

Characters and Unicode

Total characters57783
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11
2nd row20
3rd row0
4th row20
5th row20

Common Values

ValueCountFrequency (%)
20 11095
29.9%
. 8917
24.0%
0 7508
20.2%
21 4395
 
11.8%
12 1588
 
4.3%
10 1330
 
3.6%
11 1315
 
3.5%
22 622
 
1.7%
23 279
 
0.8%
13 55
 
0.1%

Length

2025-05-20T09:27:08.541008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-20T09:27:08.565785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
20 11095
29.9%
8917
24.0%
0 7508
20.2%
21 4395
 
11.8%
12 1588
 
4.3%
10 1330
 
3.6%
11 1315
 
3.5%
22 622
 
1.7%
23 279
 
0.8%
13 55
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 19933
34.5%
2 18601
32.2%
1 9998
17.3%
. 8917
15.4%
3 334
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 57783
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 19933
34.5%
2 18601
32.2%
1 9998
17.3%
. 8917
15.4%
3 334
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 57783
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 19933
34.5%
2 18601
32.2%
1 9998
17.3%
. 8917
15.4%
3 334
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 57783
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 19933
34.5%
2 18601
32.2%
1 9998
17.3%
. 8917
15.4%
3 334
 
0.6%

IN_PROFICIENCIA_LP
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
1
28562 
0
8542 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37104
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 28562
77.0%
0 8542
 
23.0%

Length

2025-05-20T09:27:08.599944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-20T09:27:08.617200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 28562
77.0%
0 8542
 
23.0%

Most occurring characters

ValueCountFrequency (%)
1 28562
77.0%
0 8542
 
23.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 28562
77.0%
0 8542
 
23.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 28562
77.0%
0 8542
 
23.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 28562
77.0%
0 8542
 
23.0%

IN_PROFICIENCIA_MT
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
1
28652 
0
8452 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37104
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 28652
77.2%
0 8452
 
22.8%

Length

2025-05-20T09:27:08.639729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-20T09:27:08.658662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 28652
77.2%
0 8452
 
22.8%

Most occurring characters

ValueCountFrequency (%)
1 28652
77.2%
0 8452
 
22.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 28652
77.2%
0 8452
 
22.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 28652
77.2%
0 8452
 
22.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 28652
77.2%
0 8452
 
22.8%

IN_AMOSTRA
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
1
37104 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37104
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 37104
100.0%

Length

2025-05-20T09:27:08.681024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-20T09:27:08.697991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 37104
100.0%

Most occurring characters

ValueCountFrequency (%)
1 37104
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 37104
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 37104
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 37104
100.0%

ESTRATO
Real number (ℝ)

High correlation 

Distinct242
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27955.839
Minimum11211
Maximum53411
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size290.0 KiB
2025-05-20T09:27:08.725714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum11211
5-th percentile12212
Q116222
median25412
Q335312
95-th percentile52312
Maximum53411
Range42200
Interquartile range (IQR)19090

Descriptive statistics

Standard deviation12914.16
Coefficient of variation (CV)0.46194858
Kurtosis-0.79314858
Mean27955.839
Median Absolute Deviation (MAD)9201
Skewness0.58534312
Sum1.0372735 × 109
Variance1.6677554 × 108
MonotonicityNot monotonic
2025-05-20T09:27:08.766045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14311 591
 
1.6%
53211 537
 
1.4%
14411 513
 
1.4%
16211 503
 
1.4%
12312 477
 
1.3%
52312 453
 
1.2%
16312 453
 
1.2%
53411 423
 
1.1%
12211 389
 
1.0%
12411 382
 
1.0%
Other values (232) 32383
87.3%
ValueCountFrequency (%)
11211 151
0.4%
11212 208
0.6%
11222 14
 
< 0.1%
11311 156
0.4%
11312 265
0.7%
11321 142
0.4%
11322 141
0.4%
11411 108
0.3%
11412 161
0.4%
11421 78
 
0.2%
ValueCountFrequency (%)
53411 423
1.1%
53221 152
 
0.4%
53211 537
1.4%
52422 49
 
0.1%
52412 290
0.8%
52411 187
 
0.5%
52322 133
 
0.4%
52321 32
 
0.1%
52312 453
1.2%
52311 163
 
0.4%

PESO_ALUNO_LP
Real number (ℝ)

High correlation  Missing 

Distinct555
Distinct (%)1.9%
Missing8600
Missing (%)23.2%
Infinite0
Infinite (%)0.0%
Mean96.319183
Minimum1
Maximum2562.5721
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size290.0 KiB
2025-05-20T09:27:08.803472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.0168919
Q17.1539342
median29.984448
Q395.48488
95-th percentile379.72604
Maximum2562.5721
Range2561.5721
Interquartile range (IQR)88.330946

Descriptive statistics

Standard deviation222.0021
Coefficient of variation (CV)2.3048586
Kurtosis58.539138
Mean96.319183
Median Absolute Deviation (MAD)26.16758
Skewness6.6047629
Sum2745482
Variance49284.931
MonotonicityNot monotonic
2025-05-20T09:27:08.845687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.016891892 180
 
0.5%
3.695842451 173
 
0.5%
9.6629019 164
 
0.4%
2.689189189 161
 
0.4%
4.701683695 156
 
0.4%
2.575061816 152
 
0.4%
12.9734415 148
 
0.4%
23.432 125
 
0.3%
12.37903226 124
 
0.3%
5.289394157 122
 
0.3%
Other values (545) 26999
72.8%
(Missing) 8600
 
23.2%
ValueCountFrequency (%)
1 12
 
< 0.1%
1.048780488 41
0.1%
1.066666667 45
0.1%
1.071428571 14
 
< 0.1%
1.076923077 33
0.1%
1.085714286 29
0.1%
1.283018868 12
 
< 0.1%
1.419354839 31
0.1%
1.445945946 12
 
< 0.1%
1.487021014 39
0.1%
ValueCountFrequency (%)
2562.572065 93
0.3%
1752.93617 47
0.1%
1483.678895 38
0.1%
1323.988724 67
0.2%
1203.093888 40
0.1%
973.5722116 43
0.1%
953.3725714 20
 
0.1%
807.9153236 34
 
0.1%
775.7481668 93
0.3%
767.7333333 15
 
< 0.1%

IN_ALFABETIZADO
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
0
22365 
1
14739 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters37104
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 22365
60.3%
1 14739
39.7%

Length

2025-05-20T09:27:08.880329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-20T09:27:08.898046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 22365
60.3%
1 14739
39.7%

Most occurring characters

ValueCountFrequency (%)
0 22365
60.3%
1 14739
39.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 22365
60.3%
1 14739
39.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 22365
60.3%
1 14739
39.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37104
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 22365
60.3%
1 14739
39.7%

PROFICIENCIA_LP
Real number (ℝ)

High correlation  Missing 

Distinct23896
Distinct (%)83.7%
Missing8542
Missing (%)23.0%
Infinite0
Infinite (%)0.0%
Mean-0.14074422
Minimum-2.523422
Maximum1.957006
Zeros0
Zeros (%)0.0%
Negative15054
Negative (%)40.6%
Memory size290.0 KiB
2025-05-20T09:27:08.924757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2.523422
5-th percentile-1.8211412
Q1-0.68312725
median-0.059131
Q30.49280025
95-th percentile1.173859
Maximum1.957006
Range4.480428
Interquartile range (IQR)1.1759275

Descriptive statistics

Standard deviation0.88267367
Coefficient of variation (CV)-6.2714739
Kurtosis-0.24626728
Mean-0.14074422
Median Absolute Deviation (MAD)0.585115
Skewness-0.36879621
Sum-4019.9363
Variance0.77911281
MonotonicityNot monotonic
2025-05-20T09:27:08.960822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.135496 57
 
0.2%
1.145569 53
 
0.1%
1.151387 46
 
0.1%
1.220768 39
 
0.1%
1.180774 37
 
0.1%
1.746931 37
 
0.1%
1.229156 34
 
0.1%
0.882387 34
 
0.1%
1.722741 31
 
0.1%
1.727951 31
 
0.1%
Other values (23886) 28163
75.9%
(Missing) 8542
 
23.0%
ValueCountFrequency (%)
-2.523422 1
< 0.1%
-2.510062 1
< 0.1%
-2.507428 1
< 0.1%
-2.504855 1
< 0.1%
-2.503955 1
< 0.1%
-2.503474 1
< 0.1%
-2.5013 1
< 0.1%
-2.49923 1
< 0.1%
-2.495165 1
< 0.1%
-2.49354 1
< 0.1%
ValueCountFrequency (%)
1.957006 6
 
< 0.1%
1.936695 9
< 0.1%
1.91901 8
< 0.1%
1.911367 9
< 0.1%
1.903294 3
 
< 0.1%
1.894235 5
 
< 0.1%
1.893935 11
< 0.1%
1.878284 11
< 0.1%
1.870384 16
< 0.1%
1.869622 7
< 0.1%

ERRO_PADRAO_LP
Real number (ℝ)

High correlation  Missing 

Distinct22413
Distinct (%)78.5%
Missing8542
Missing (%)23.0%
Infinite0
Infinite (%)0.0%
Mean0.33063538
Minimum0.217346
Maximum0.628324
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size290.0 KiB
2025-05-20T09:27:08.996773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.217346
5-th percentile0.25079525
Q10.279745
median0.312886
Q30.368273
95-th percentile0.4622359
Maximum0.628324
Range0.410978
Interquartile range (IQR)0.088528

Descriptive statistics

Standard deviation0.067124583
Coefficient of variation (CV)0.20301694
Kurtosis0.74875261
Mean0.33063538
Median Absolute Deviation (MAD)0.039592
Skewness1.0325674
Sum9443.6078
Variance0.0045057097
MonotonicityNot monotonic
2025-05-20T09:27:09.034915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.437821 57
 
0.2%
0.432994 53
 
0.1%
0.440904 46
 
0.1%
0.450363 39
 
0.1%
0.567809 37
 
0.1%
0.438259 37
 
0.1%
0.444735 34
 
0.1%
0.372528 34
 
0.1%
0.561455 31
 
0.1%
0.56937 31
 
0.1%
Other values (22403) 28163
75.9%
(Missing) 8542
 
23.0%
ValueCountFrequency (%)
0.217346 1
< 0.1%
0.217647 1
< 0.1%
0.219105 1
< 0.1%
0.219472 1
< 0.1%
0.219829 1
< 0.1%
0.220549 1
< 0.1%
0.220591 1
< 0.1%
0.220743 1
< 0.1%
0.221158 1
< 0.1%
0.221659 1
< 0.1%
ValueCountFrequency (%)
0.628324 1
 
< 0.1%
0.611081 1
 
< 0.1%
0.56937 31
0.1%
0.569091 22
0.1%
0.567809 37
0.1%
0.566217 3
 
< 0.1%
0.566083 16
< 0.1%
0.565774 18
< 0.1%
0.564506 20
0.1%
0.563974 9
 
< 0.1%

PROFICIENCIA_LP_SAEB
Real number (ℝ)

High correlation  Missing 

Distinct23986
Distinct (%)84.0%
Missing8542
Missing (%)23.0%
Infinite0
Infinite (%)0.0%
Mean740.42475
Minimum608.40933
Maximum856.65336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size290.0 KiB
2025-05-20T09:27:09.073206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum608.40933
5-th percentile647.3201
Q1710.3733
median744.94664
Q3775.52711
95-th percentile813.26206
Maximum856.65336
Range248.24403
Interquartile range (IQR)65.15381

Descriptive statistics

Standard deviation48.905707
Coefficient of variation (CV)0.066050881
Kurtosis-0.24626727
Mean740.42475
Median Absolute Deviation (MAD)32.41904
Skewness-0.36879622
Sum21148012
Variance2391.7682
MonotonicityNot monotonic
2025-05-20T09:27:09.112932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
811.136512 57
 
0.2%
811.694655 53
 
0.1%
812.016965 46
 
0.1%
815.861154 39
 
0.1%
845.013899 37
 
0.1%
813.645213 37
 
0.1%
816.325894 34
 
0.1%
797.112695 34
 
0.1%
843.962262 31
 
0.1%
843.673605 31
 
0.1%
Other values (23976) 28163
75.9%
(Missing) 8542
 
23.0%
ValueCountFrequency (%)
608.409331 1
< 0.1%
609.149533 1
< 0.1%
609.295467 1
< 0.1%
609.438045 1
< 0.1%
609.487897 1
< 0.1%
609.514572 1
< 0.1%
609.635008 1
< 0.1%
609.749689 1
< 0.1%
609.974923 1
< 0.1%
610.064986 1
< 0.1%
ValueCountFrequency (%)
856.653362 6
 
< 0.1%
855.528048 9
< 0.1%
854.548188 8
< 0.1%
854.124701 9
< 0.1%
853.677388 3
 
< 0.1%
853.175481 5
 
< 0.1%
853.158867 11
< 0.1%
852.291709 11
< 0.1%
851.853986 16
< 0.1%
851.811757 7
< 0.1%

ERRO_PADRAO_LP_SAEB
Real number (ℝ)

High correlation  Missing 

Distinct23948
Distinct (%)83.8%
Missing8542
Missing (%)23.0%
Infinite0
Infinite (%)0.0%
Mean18.319293
Minimum12.04237
Maximum34.813104
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size290.0 KiB
2025-05-20T09:27:09.153607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum12.04237
5-th percentile13.895619
Q115.499627
median17.335862
Q320.404682
95-th percentile25.610809
Maximum34.813104
Range22.770734
Interquartile range (IQR)4.9050553

Descriptive statistics

Standard deviation3.7191273
Coefficient of variation (CV)0.20301697
Kurtosis0.74875547
Mean18.319293
Median Absolute Deviation (MAD)2.1936445
Skewness1.0325679
Sum523235.64
Variance13.831908
MonotonicityNot monotonic
2025-05-20T09:27:09.193169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.258046 57
 
0.2%
23.990606 53
 
0.1%
24.428888 46
 
0.1%
24.952945 39
 
0.1%
31.460232 37
 
0.1%
24.282344 37
 
0.1%
24.64114 34
 
0.1%
20.640412 34
 
0.1%
31.108184 31
 
0.1%
31.546729 31
 
0.1%
Other values (23938) 28163
75.9%
(Missing) 8542
 
23.0%
ValueCountFrequency (%)
12.04237 1
< 0.1%
12.059036 1
< 0.1%
12.139788 1
< 0.1%
12.160146 1
< 0.1%
12.179907 1
< 0.1%
12.219806 1
< 0.1%
12.222147 1
< 0.1%
12.230547 1
< 0.1%
12.253532 1
< 0.1%
12.281318 1
< 0.1%
ValueCountFrequency (%)
34.813104 1
 
< 0.1%
33.857754 1
 
< 0.1%
31.546729 31
0.1%
31.531254 22
0.1%
31.460232 37
0.1%
31.372027 3
 
< 0.1%
31.364598 16
< 0.1%
31.347475 18
< 0.1%
31.277226 20
0.1%
31.247724 9
 
< 0.1%

PESO_ALUNO_MT
Real number (ℝ)

High correlation  Missing 

Distinct557
Distinct (%)1.9%
Missing8513
Missing (%)22.9%
Infinite0
Infinite (%)0.0%
Mean96.026092
Minimum1
Maximum2389.7644
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size290.0 KiB
2025-05-20T09:27:09.230746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.0220152
Q16.9727273
median29.05344
Q393.151551
95-th percentile387.09128
Maximum2389.7644
Range2388.7644
Interquartile range (IQR)86.178824

Descriptive statistics

Standard deviation219.76822
Coefficient of variation (CV)2.2886302
Kurtosis51.336877
Mean96.026092
Median Absolute Deviation (MAD)25.10756
Skewness6.2482691
Sum2745482
Variance48298.069
MonotonicityNot monotonic
2025-05-20T09:27:09.269323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.022015241 179
 
0.5%
9.773284314 170
 
0.5%
3.710049423 168
 
0.5%
2.696020322 161
 
0.4%
4.644207534 160
 
0.4%
2.517265193 152
 
0.4%
12.97122609 152
 
0.4%
22.70542636 129
 
0.3%
3.575620767 128
 
0.3%
5.224733475 126
 
0.3%
Other values (547) 27066
72.9%
(Missing) 8513
 
22.9%
ValueCountFrequency (%)
1 12
 
< 0.1%
1.021276596 47
0.1%
1.049723757 29
0.1%
1.071428571 14
 
< 0.1%
1.098039216 20
0.1%
1.102564103 39
0.1%
1.192982456 15
 
< 0.1%
1.272727273 11
 
< 0.1%
1.419354839 31
0.1%
1.445945946 12
 
< 0.1%
ValueCountFrequency (%)
2389.764441 99
0.3%
1791.043478 46
0.1%
1538.281484 9
 
< 0.1%
1383.626675 42
0.1%
1374.768952 65
0.2%
1249.237319 38
 
0.1%
996.5111195 43
0.1%
857.5792257 32
 
0.1%
805.0916115 89
0.2%
739.6599581 25
 
0.1%

PROFICIENCIA_MT
Real number (ℝ)

High correlation  Missing 

Distinct24144
Distinct (%)84.3%
Missing8452
Missing (%)22.8%
Infinite0
Infinite (%)0.0%
Mean0.046749308
Minimum-2.724693
Maximum2.132756
Zeros0
Zeros (%)0.0%
Negative13591
Negative (%)36.6%
Memory size290.0 KiB
2025-05-20T09:27:09.304665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2.724693
5-th percentile-1.4783693
Q1-0.5809155
median0.065048
Q30.698924
95-th percentile1.54093
Maximum2.132756
Range4.857449
Interquartile range (IQR)1.2798395

Descriptive statistics

Standard deviation0.90265261
Coefficient of variation (CV)19.308363
Kurtosis-0.43190533
Mean0.046749308
Median Absolute Deviation (MAD)0.6401955
Skewness-0.13132919
Sum1339.4612
Variance0.81478173
MonotonicityNot monotonic
2025-05-20T09:27:09.339951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.736825 88
 
0.2%
1.749373 77
 
0.2%
1.699479 76
 
0.2%
1.884551 68
 
0.2%
1.674265 61
 
0.2%
1.649286 60
 
0.2%
1.783352 59
 
0.2%
1.690538 54
 
0.1%
1.755732 51
 
0.1%
1.792819 50
 
0.1%
Other values (24134) 28008
75.5%
(Missing) 8452
 
22.8%
ValueCountFrequency (%)
-2.724693 1
< 0.1%
-2.716178 1
< 0.1%
-2.677493 1
< 0.1%
-2.66662 1
< 0.1%
-2.648271 1
< 0.1%
-2.644959 1
< 0.1%
-2.60541 2
< 0.1%
-2.598609 1
< 0.1%
-2.594887 1
< 0.1%
-2.591174 1
< 0.1%
ValueCountFrequency (%)
2.132756 14
 
< 0.1%
2.025852 8
 
< 0.1%
2.021137 34
0.1%
2.020824 45
0.1%
1.988539 38
0.1%
1.940068 20
 
0.1%
1.920026 26
 
0.1%
1.918892 29
0.1%
1.914995 44
0.1%
1.884551 68
0.2%

ERRO_PADRAO_MT
Real number (ℝ)

High correlation  Missing 

Distinct22325
Distinct (%)77.9%
Missing8452
Missing (%)22.8%
Infinite0
Infinite (%)0.0%
Mean0.44541509
Minimum0.343285
Maximum0.720035
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size290.0 KiB
2025-05-20T09:27:09.377771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.343285
5-th percentile0.3777232
Q10.40618675
median0.434102
Q30.47227825
95-th percentile0.56108685
Maximum0.720035
Range0.37675
Interquartile range (IQR)0.0660915

Descriptive statistics

Standard deviation0.054207728
Coefficient of variation (CV)0.1217016
Kurtosis0.77620553
Mean0.44541509
Median Absolute Deviation (MAD)0.031408
Skewness1.0236414
Sum12762.033
Variance0.0029384778
MonotonicityNot monotonic
2025-05-20T09:27:09.416483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.603939 88
 
0.2%
0.594143 77
 
0.2%
0.587532 76
 
0.2%
0.581085 68
 
0.2%
0.61146 61
 
0.2%
0.59715 60
 
0.2%
0.592807 59
 
0.2%
0.599841 54
 
0.1%
0.581447 51
 
0.1%
0.584899 50
 
0.1%
Other values (22315) 28008
75.5%
(Missing) 8452
 
22.8%
ValueCountFrequency (%)
0.343285 1
< 0.1%
0.343404 1
< 0.1%
0.344965 1
< 0.1%
0.345929 1
< 0.1%
0.346065 1
< 0.1%
0.346444 1
< 0.1%
0.3465 1
< 0.1%
0.347027 1
< 0.1%
0.347259 1
< 0.1%
0.347677 1
< 0.1%
ValueCountFrequency (%)
0.720035 1
< 0.1%
0.699024 1
< 0.1%
0.696225 1
< 0.1%
0.682566 1
< 0.1%
0.668014 1
< 0.1%
0.661421 1
< 0.1%
0.649606 1
< 0.1%
0.649146 1
< 0.1%
0.644701 1
< 0.1%
0.643153 1
< 0.1%

PROFICIENCIA_MT_SAEB
Real number (ℝ)

High correlation  Missing 

Distinct24263
Distinct (%)84.7%
Missing8452
Missing (%)22.8%
Infinite0
Infinite (%)0.0%
Mean751.28347
Minimum594.61478
Maximum869.20476
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size290.0 KiB
2025-05-20T09:27:09.452717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum594.61478
5-th percentile665.06901
Q1715.80178
median752.31788
Q3788.15069
95-th percentile835.74898
Maximum869.20476
Range274.58998
Interquartile range (IQR)72.348909

Descriptive statistics

Standard deviation51.026653
Coefficient of variation (CV)0.067919308
Kurtosis-0.43190535
Mean751.28347
Median Absolute Deviation (MAD)36.190059
Skewness-0.1313292
Sum21525774
Variance2603.7193
MonotonicityNot monotonic
2025-05-20T09:27:09.493309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
846.822876 88
 
0.2%
847.532204 77
 
0.2%
844.711731 76
 
0.2%
855.173787 68
 
0.2%
843.286402 61
 
0.2%
841.874335 60
 
0.2%
849.453051 59
 
0.2%
844.206272 54
 
0.1%
847.891674 51
 
0.1%
849.988184 50
 
0.1%
Other values (24253) 28008
75.5%
(Missing) 8452
 
22.8%
ValueCountFrequency (%)
594.614776 1
< 0.1%
595.096115 1
< 0.1%
597.282938 1
< 0.1%
597.897596 1
< 0.1%
598.934873 1
< 0.1%
599.122084 1
< 0.1%
601.357798 2
< 0.1%
601.742242 1
< 0.1%
601.952645 1
< 0.1%
602.162537 1
< 0.1%
ValueCountFrequency (%)
869.204758 14
 
< 0.1%
863.161491 8
 
< 0.1%
862.894975 34
0.1%
862.877256 45
0.1%
861.05218 38
0.1%
858.31212 20
 
0.1%
857.179195 26
 
0.1%
857.115061 29
0.1%
856.89478 44
0.1%
855.173787 68
0.2%

ERRO_PADRAO_MT_SAEB
Real number (ℝ)

High correlation  Missing 

Distinct24225
Distinct (%)84.5%
Missing8452
Missing (%)22.8%
Infinite0
Infinite (%)0.0%
Mean25.179167
Minimum19.405795
Maximum40.703353
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size290.0 KiB
2025-05-20T09:27:09.532003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum19.405795
5-th percentile21.35258
Q122.961606
median24.539627
Q326.697735
95-th percentile31.71805
Maximum40.703353
Range21.297558
Interquartile range (IQR)3.7361285

Descriptive statistics

Standard deviation3.0643447
Coefficient of variation (CV)0.12170159
Kurtosis0.7762048
Mean25.179167
Median Absolute Deviation (MAD)1.775469
Skewness1.0236411
Sum721433.51
Variance9.3902084
MonotonicityNot monotonic
2025-05-20T09:27:09.566926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.140451 88
 
0.2%
33.586714 77
 
0.2%
33.212988 76
 
0.2%
32.848545 68
 
0.2%
34.565637 61
 
0.2%
33.756678 60
 
0.2%
33.511182 59
 
0.2%
33.90883 54
 
0.1%
32.86898 51
 
0.1%
33.064126 50
 
0.1%
Other values (24215) 28008
75.5%
(Missing) 8452
 
22.8%
ValueCountFrequency (%)
19.405795 1
< 0.1%
19.412507 1
< 0.1%
19.500768 1
< 0.1%
19.55526 1
< 0.1%
19.562933 1
< 0.1%
19.584387 1
< 0.1%
19.587517 1
< 0.1%
19.617318 1
< 0.1%
19.630462 1
< 0.1%
19.654086 1
< 0.1%
ValueCountFrequency (%)
40.703353 1
< 0.1%
39.515613 1
< 0.1%
39.357381 1
< 0.1%
38.585251 1
< 0.1%
37.762604 1
< 0.1%
37.389926 1
< 0.1%
36.72202 1
< 0.1%
36.696007 1
< 0.1%
36.444754 1
< 0.1%
36.357246 1
< 0.1%

Interactions

2025-05-20T09:27:04.630702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:44.216970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:44.991219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:45.895679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:46.745730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:47.701513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:48.552908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:49.287712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:50.203310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:50.982303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:51.772252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:52.720748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:53.464242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:54.271057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:55.213102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:56.003128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:56.795174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:57.570623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:58.550312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:59.264462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:59.977014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:27:00.825681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:27:01.522923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-20T09:27:02.984202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:27:03.904372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:27:04.656530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-20T09:26:45.925678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:46.773380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-20T09:26:48.580506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:49.314836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:50.231675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:51.012215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:51.801305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:52.747285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:53.494404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:54.299085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-20T09:26:51.866175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-20T09:26:50.055236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:50.833873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:51.620896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:52.571498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:53.319528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:54.122640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:54.906475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:55.854602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:56.644757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:57.395221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:58.416243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:59.106550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:59.825572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:27:00.691198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:27:01.395206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:27:02.114952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:27:02.844779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:27:03.759352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:27:04.495513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:27:05.237686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:44.875213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:45.770203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:46.617386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:47.581838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:48.400671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:49.174585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:50.084978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:50.863204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:51.651087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:52.600581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:53.347869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:54.152203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:54.936179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:55.883567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:56.674354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:57.443016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:58.442504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:59.131655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:59.857219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:27:00.717892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:27:01.420459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:27:02.140716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:27:02.873663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:27:03.789063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:27:04.522113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:27:05.263092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:44.903161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:45.799509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:46.648001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:47.608825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:48.458952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:49.200659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:50.112909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-20T09:26:54.965291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:55.913516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:56.703436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:57.478909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:58.467546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:59.183685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-20T09:27:03.818198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:27:04.548336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:27:05.290819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:26:44.935428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-20T09:27:00.799991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:27:01.498617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:27:02.222454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:27:02.957522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:27:03.877010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:27:04.604158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-05-20T09:27:09.810997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
CO_CONCEITO_COESAOCO_CONCEITO_PONTUACAOCO_CONCEITO_Q1_LPCO_CONCEITO_Q1_MTCO_CONCEITO_Q2_LPCO_CONCEITO_Q2_MTCO_CONCEITO_SEGMENTACAOCO_CONCEITO_SEQUENCIACO_RESPOSTA_TEXTOCO_TEXTO_GRAFIAERRO_PADRAO_LPERRO_PADRAO_LP_SAEBERRO_PADRAO_MTERRO_PADRAO_MT_SAEBESTRATOID_ALUNOID_AREAID_BLOCO_1_LPID_BLOCO_1_MTID_BLOCO_2_LPID_BLOCO_2_MTID_CADERNO_LPID_CADERNO_MTID_ESCOLAID_LOCALIZACAOID_MUNICIPIOID_REGIAOID_TURMAID_UFIN_ALFABETIZADOIN_PREENCHIMENTO_LPIN_PREENCHIMENTO_MTIN_PRESENCA_LPIN_PRESENCA_MTIN_PROFICIENCIA_LPIN_PROFICIENCIA_MTIN_PUBLICAIN_SITUACAO_CENSONU_BLOCO_1_ABERTA_LPNU_BLOCO_1_ABERTA_MTNU_BLOCO_2_ABERTA_LPNU_BLOCO_2_ABERTA_MTPESO_ALUNO_LPPESO_ALUNO_MTPROFICIENCIA_LPPROFICIENCIA_LP_SAEBPROFICIENCIA_MTPROFICIENCIA_MT_SAEB
CO_CONCEITO_COESAO1.0000.7220.5490.3730.5440.3720.7330.7701.0000.7480.2060.2060.0720.0720.0770.0810.0620.0180.0180.0250.0250.0280.0280.0340.0790.0760.0700.0350.0770.7230.9060.7010.9000.6990.9070.7010.2710.0450.0180.0180.0250.0250.0360.0340.4230.4230.2330.233
CO_CONCEITO_PONTUACAO0.7221.0000.5420.3720.5380.3710.7220.7221.0000.7150.1940.1940.0880.0880.0770.0790.0640.0170.0170.0270.0270.0280.0280.0370.0810.0790.0680.0360.0780.6790.9060.7010.9000.6990.9070.7010.2930.0460.0170.0170.0270.0270.0410.0440.4090.4090.2330.233
CO_CONCEITO_Q1_LP0.5490.5421.0000.3430.5410.3420.5490.5430.7600.5520.2540.2540.0800.0800.0510.0540.0550.1270.1270.0870.0870.1050.1050.0300.0590.0530.0510.0300.0520.6800.9470.7280.9410.7270.9470.7280.2290.0470.1270.1270.0870.0870.0230.0180.4230.4230.2160.216
CO_CONCEITO_Q1_MT0.3730.3720.3431.0000.3400.3310.3750.3710.5200.3730.0670.0670.1130.1130.0360.0410.0700.3560.3560.1510.1510.1670.1670.0340.0560.0350.0370.0330.0380.4650.7350.9550.7310.9550.7360.9550.1750.0420.3560.3560.1510.1510.0310.0390.1320.1320.2040.204
CO_CONCEITO_Q2_LP0.5440.5380.5410.3401.0000.3350.5470.5380.7550.5470.2500.2500.0790.0790.0470.0550.0570.0800.0800.1290.1290.1020.1020.0250.0660.0510.0490.0300.0480.6810.9220.7120.9170.7100.9230.7120.2200.0450.0800.0800.1290.1290.0240.0200.4150.4150.2120.212
CO_CONCEITO_Q2_MT0.3720.3710.3420.3310.3351.0000.3740.3700.5190.3720.0590.0590.1010.1010.0360.0410.0560.1490.1490.3500.3500.1980.1980.0340.0370.0360.0340.0410.0380.4570.7300.9480.7260.9480.7310.9480.1740.0470.1490.1490.3500.3500.0340.0260.1240.1240.1940.194
CO_CONCEITO_SEGMENTACAO0.7330.7220.5490.3750.5470.3741.0000.7151.0000.7640.2350.2350.0830.0830.0790.0800.0700.0170.0170.0260.0260.0300.0300.0350.0880.0760.0690.0380.0800.7210.9060.7010.9000.6990.9070.7010.2960.0460.0160.0170.0260.0260.0380.0350.4370.4370.2430.243
CO_CONCEITO_SEQUENCIA0.7700.7220.5430.3710.5380.3700.7151.0001.0000.7160.2210.2210.0730.0730.0790.0830.0620.0180.0180.0260.0260.0280.0280.0350.0760.0780.0720.0360.0780.6940.9060.7010.9000.6990.9070.7010.2610.0460.0180.0180.0260.0260.0380.0370.4270.4270.2270.227
CO_RESPOSTA_TEXTO1.0001.0000.7600.5200.7550.5191.0001.0001.0001.0000.1690.1690.0980.0980.0860.0960.0590.0230.0230.0340.0340.0370.0370.0420.0580.0860.0780.0460.0870.6540.9060.7010.9000.6990.9070.7010.2170.0460.0230.0230.0340.0340.0430.0410.5200.5200.2970.297
CO_TEXTO_GRAFIA0.7480.7150.5520.3730.5470.3720.7640.7161.0001.0000.1960.1960.0740.0740.0730.0790.0610.0170.0170.0250.0250.0270.0270.0320.0790.0750.0690.0360.0730.7220.9060.7010.9000.6990.9070.7010.2490.0450.0170.0170.0250.0250.0350.0320.4180.4180.2330.233
ERRO_PADRAO_LP0.2060.1940.2540.0670.2500.0590.2350.2210.1690.1961.0001.0000.2170.2170.0590.0550.0200.0550.055-0.002-0.0020.0100.0100.0030.0430.0550.044-0.0000.0550.4441.0000.0411.0000.0381.0000.0410.1580.0150.0560.055-0.000-0.0020.0190.0190.3590.3590.2540.254
ERRO_PADRAO_LP_SAEB0.2060.1940.2540.0670.2500.0590.2350.2210.1690.1961.0001.0000.2170.2170.0590.0550.0200.0550.055-0.002-0.0020.0100.0100.0030.0430.0550.044-0.0000.0550.4441.0000.0411.0000.0381.0000.0410.1580.0150.0560.055-0.000-0.0020.0190.0190.3590.3590.2540.254
ERRO_PADRAO_MT0.0720.0880.0800.1130.0790.1010.0830.0730.0980.0740.2170.2171.0001.0000.0290.0270.0490.0330.0330.0470.0470.0030.003-0.0020.0260.0270.0320.0020.0280.1130.0191.0000.0181.0000.0201.0000.0360.0080.0340.0330.0470.0470.0220.0190.0060.0060.1340.134
ERRO_PADRAO_MT_SAEB0.0720.0880.0800.1130.0790.1010.0830.0730.0980.0740.2170.2171.0001.0000.0290.0270.0490.0330.0330.0470.0470.0030.003-0.0020.0260.0270.0320.0020.0280.1130.0191.0000.0181.0000.0201.0000.0360.0080.0340.0330.0470.0470.0220.0190.0060.0060.1340.134
ESTRATO0.0770.0770.0510.0360.0470.0360.0790.0790.0860.0730.0590.0590.0290.0291.0000.9980.135-0.009-0.0090.0100.0100.0010.0010.0460.0610.9990.9650.0000.9990.0940.0750.0790.0760.0810.0750.0790.0990.026-0.010-0.0090.0090.0100.3530.3520.1250.1250.0960.096
ID_ALUNO0.0810.0790.0540.0410.0550.0410.0800.0830.0960.0790.0550.0550.0270.0270.9981.0000.190-0.010-0.0100.0090.0090.0020.0020.0480.0980.9980.911-0.0000.9990.1040.0970.1040.0970.1080.0980.1040.1280.018-0.011-0.0100.0080.0090.3550.3540.1160.1160.0880.088
ID_AREA0.0620.0640.0550.0700.0570.0560.0700.0620.0590.0610.0200.0200.0490.0490.1350.1901.0000.0260.0260.0360.0360.0670.0670.0730.2190.1510.1250.0480.1310.0290.0530.0490.0530.0490.0530.0490.0720.0000.0260.0260.0360.0360.1660.1720.0260.0260.0430.043
ID_BLOCO_1_LP0.0180.0170.1270.3560.0800.1490.0170.0180.0230.0170.0550.0550.0330.033-0.009-0.0100.0261.0001.000-0.162-0.1620.3230.3230.0000.029-0.0120.0100.009-0.0100.0240.0350.0220.0350.0220.0350.0220.0190.0220.9991.000-0.165-0.162-0.009-0.011-0.010-0.0100.0110.011
ID_BLOCO_1_MT0.0180.0170.1270.3560.0800.1490.0170.0180.0230.0170.0550.0550.0330.033-0.009-0.0100.0261.0001.000-0.162-0.1620.3230.3230.0000.029-0.0120.0100.009-0.0100.0240.0350.0220.0350.0220.0350.0220.0190.0220.9991.000-0.165-0.162-0.009-0.011-0.010-0.0100.0110.011
ID_BLOCO_2_LP0.0250.0270.0870.1510.1290.3500.0260.0260.0340.025-0.002-0.0020.0470.0470.0100.0090.036-0.162-0.1621.0001.000-0.053-0.053-0.0020.0540.0100.009-0.0150.0100.0440.0430.0310.0440.0310.0430.0320.0000.017-0.164-0.1620.9971.000-0.017-0.0180.0150.0150.0180.018
ID_BLOCO_2_MT0.0250.0270.0870.1510.1290.3500.0260.0260.0340.025-0.002-0.0020.0470.0470.0100.0090.036-0.162-0.1621.0001.000-0.053-0.053-0.0020.0540.0100.009-0.0150.0100.0440.0430.0310.0440.0310.0430.0320.0000.017-0.164-0.1620.9971.000-0.017-0.0180.0150.0150.0180.018
ID_CADERNO_LP0.0280.0280.1050.1670.1020.1980.0300.0280.0370.0270.0100.0100.0030.0030.0010.0020.0670.3230.323-0.053-0.0531.0001.000-0.0050.0710.0030.024-0.0570.0010.0520.0490.0380.0500.0370.0490.0380.0350.0270.3250.323-0.049-0.053-0.018-0.020-0.021-0.021-0.006-0.006
ID_CADERNO_MT0.0280.0280.1050.1670.1020.1980.0300.0280.0370.0270.0100.0100.0030.0030.0010.0020.0670.3230.323-0.053-0.0531.0001.000-0.0050.0710.0030.024-0.0570.0010.0520.0490.0380.0500.0370.0490.0380.0350.0270.3250.323-0.049-0.053-0.018-0.020-0.021-0.021-0.006-0.006
ID_ESCOLA0.0340.0370.0300.0340.0250.0340.0350.0350.0420.0320.0030.003-0.002-0.0020.0460.0480.0730.0000.000-0.002-0.002-0.005-0.0051.0000.1190.0460.1380.0070.0470.0430.0470.0550.0460.0520.0460.0550.0910.0270.0010.000-0.002-0.002-0.014-0.012-0.002-0.002-0.006-0.006
ID_LOCALIZACAO0.0790.0810.0590.0560.0660.0370.0880.0760.0580.0790.0430.0430.0260.0260.0610.0980.2190.0290.0290.0540.0540.0710.0710.1191.0000.0790.0330.0700.0610.0420.0270.0280.0280.0290.0270.0280.1990.0000.0290.0290.0540.0540.1160.1160.0870.0870.0640.064
ID_MUNICIPIO0.0760.0790.0530.0350.0510.0360.0760.0780.0860.0750.0550.0550.0270.0270.9990.9980.151-0.012-0.0120.0100.0100.0030.0030.0460.0791.0000.976-0.0000.9990.1100.0630.0710.0640.0730.0630.0710.0920.022-0.012-0.0120.0100.0100.3530.3520.1160.1160.0890.089
ID_REGIAO0.0700.0680.0510.0370.0490.0340.0690.0720.0780.0690.0440.0440.0320.0320.9650.9110.1250.0100.0100.0090.0090.0240.0240.1380.0330.9761.0000.1050.9550.0920.0470.0520.0480.0520.0470.0520.0880.0230.0100.0100.0080.0090.2390.2290.0830.0830.0660.066
ID_TURMA0.0350.0360.0300.0330.0300.0410.0380.0360.0460.036-0.000-0.0000.0020.0020.000-0.0000.0480.0090.009-0.015-0.015-0.057-0.0570.0070.070-0.0000.1051.0000.0010.0620.0520.0570.0530.0530.0520.0570.0620.0170.0080.009-0.015-0.0150.000-0.006-0.002-0.002-0.014-0.014
ID_UF0.0770.0780.0520.0380.0480.0380.0800.0780.0870.0730.0550.0550.0280.0280.9990.9990.131-0.010-0.0100.0100.0100.0010.0010.0470.0610.9990.9550.0011.0000.0970.0800.0870.0820.0890.0810.0860.0950.026-0.011-0.0100.0100.0100.3530.3520.1160.1160.0890.089
IN_ALFABETIZADO0.7230.6790.6800.4650.6810.4570.7210.6940.6540.7220.4440.4440.1130.1130.0940.1040.0290.0240.0240.0440.0440.0520.0520.0430.0420.1100.0920.0620.0971.0000.4430.3610.4370.3590.4440.3610.1470.0160.0230.0240.0440.0440.0510.0470.8970.8970.5230.523
IN_PREENCHIMENTO_LP0.9060.9060.9470.7350.9220.7300.9060.9060.9060.9061.0001.0000.0190.0190.0750.0970.0530.0350.0350.0430.0430.0490.0490.0470.0270.0630.0470.0520.0800.4431.0000.7550.9870.7540.9990.7550.1460.0480.0340.0350.0430.0431.0000.0001.0001.0000.0640.064
IN_PREENCHIMENTO_MT0.7010.7010.7280.9550.7120.9480.7010.7010.7010.7010.0410.0411.0001.0000.0790.1040.0490.0220.0220.0310.0310.0380.0380.0550.0280.0710.0520.0570.0870.3610.7551.0000.7500.9830.7550.9990.1470.0440.0220.0220.0310.0310.0261.0000.0860.0861.0001.000
IN_PRESENCA_LP0.9000.9000.9410.7310.9170.7260.9000.9000.9000.9001.0001.0000.0180.0180.0760.0970.0530.0350.0350.0440.0440.0500.0500.0460.0280.0640.0480.0530.0820.4370.9870.7501.0000.7580.9850.7510.1500.0490.0350.0350.0440.0441.0000.0001.0001.0000.0620.062
IN_PRESENCA_MT0.6990.6990.7270.9550.7100.9480.6990.6990.6990.6990.0380.0381.0001.0000.0810.1080.0490.0220.0220.0310.0310.0370.0370.0520.0290.0730.0520.0530.0890.3590.7540.9830.7581.0000.7540.9830.1520.0450.0220.0220.0310.0310.0291.0000.0840.0841.0001.000
IN_PROFICIENCIA_LP0.9070.9070.9470.7360.9230.7310.9070.9070.9070.9071.0001.0000.0200.0200.0750.0980.0530.0350.0350.0430.0430.0490.0490.0460.0270.0630.0470.0520.0810.4440.9990.7550.9850.7541.0000.7550.1460.0480.0340.0350.0430.0431.0000.0001.0001.0000.0660.066
IN_PROFICIENCIA_MT0.7010.7010.7280.9550.7120.9480.7010.7010.7010.7010.0410.0411.0001.0000.0790.1040.0490.0220.0220.0320.0320.0380.0380.0550.0280.0710.0520.0570.0860.3610.7550.9990.7510.9830.7551.0000.1470.0440.0220.0220.0320.0320.0261.0000.0860.0861.0001.000
IN_PUBLICA0.2710.2930.2290.1750.2200.1740.2960.2610.2170.2490.1580.1580.0360.0360.0990.1280.0720.0190.0190.0000.0000.0350.0350.0910.1990.0920.0880.0620.0950.1470.1460.1470.1500.1520.1460.1471.0000.0200.0200.0190.0000.0000.0950.1020.3330.3330.2440.244
IN_SITUACAO_CENSO0.0450.0460.0470.0420.0450.0470.0460.0460.0460.0450.0150.0150.0080.0080.0260.0180.0000.0220.0220.0170.0170.0270.0270.0270.0000.0220.0230.0170.0260.0160.0480.0440.0490.0450.0480.0440.0201.0000.0220.0220.0170.0171.0001.0000.0220.0220.0120.012
NU_BLOCO_1_ABERTA_LP0.0180.0170.1270.3560.0800.1490.0160.0180.0230.0170.0560.0560.0340.034-0.010-0.0110.0260.9990.999-0.164-0.1640.3250.3250.0010.029-0.0120.0100.008-0.0110.0230.0340.0220.0350.0220.0340.0220.0200.0221.0000.999-0.163-0.164-0.009-0.011-0.011-0.0110.0110.011
NU_BLOCO_1_ABERTA_MT0.0180.0170.1270.3560.0800.1490.0170.0180.0230.0170.0550.0550.0330.033-0.009-0.0100.0261.0001.000-0.162-0.1620.3230.3230.0000.029-0.0120.0100.009-0.0100.0240.0350.0220.0350.0220.0350.0220.0190.0220.9991.000-0.165-0.162-0.009-0.011-0.010-0.0100.0110.011
NU_BLOCO_2_ABERTA_LP0.0250.0270.0870.1510.1290.3500.0260.0260.0340.025-0.000-0.0000.0470.0470.0090.0080.036-0.165-0.1650.9970.997-0.049-0.049-0.0020.0540.0100.008-0.0150.0100.0440.0430.0310.0440.0310.0430.0320.0000.017-0.163-0.1651.0000.997-0.018-0.0180.0140.0140.0180.018
NU_BLOCO_2_ABERTA_MT0.0250.0270.0870.1510.1290.3500.0260.0260.0340.025-0.002-0.0020.0470.0470.0100.0090.036-0.162-0.1621.0001.000-0.053-0.053-0.0020.0540.0100.009-0.0150.0100.0440.0430.0310.0440.0310.0430.0320.0000.017-0.164-0.1620.9971.000-0.017-0.0180.0150.0150.0180.018
PESO_ALUNO_LP0.0360.0410.0230.0310.0240.0340.0380.0380.0430.0350.0190.0190.0220.0220.3530.3550.166-0.009-0.009-0.017-0.017-0.018-0.018-0.0140.1160.3530.2390.0000.3530.0511.0000.0261.0000.0291.0000.0260.0951.000-0.009-0.009-0.018-0.0171.0000.9990.0340.0340.0280.028
PESO_ALUNO_MT0.0340.0440.0180.0390.0200.0260.0350.0370.0410.0320.0190.0190.0190.0190.3520.3540.172-0.011-0.011-0.018-0.018-0.020-0.020-0.0120.1160.3520.229-0.0060.3520.0470.0001.0000.0001.0000.0001.0000.1021.000-0.011-0.011-0.018-0.0180.9991.0000.0340.0340.0270.027
PROFICIENCIA_LP0.4230.4090.4230.1320.4150.1240.4370.4270.5200.4180.3590.3590.0060.0060.1250.1160.026-0.010-0.0100.0150.015-0.021-0.021-0.0020.0870.1160.083-0.0020.1160.8971.0000.0861.0000.0841.0000.0860.3330.022-0.011-0.0100.0140.0150.0340.0341.0001.0000.6370.637
PROFICIENCIA_LP_SAEB0.4230.4090.4230.1320.4150.1240.4370.4270.5200.4180.3590.3590.0060.0060.1250.1160.026-0.010-0.0100.0150.015-0.021-0.021-0.0020.0870.1160.083-0.0020.1160.8971.0000.0861.0000.0841.0000.0860.3330.022-0.011-0.0100.0140.0150.0340.0341.0001.0000.6370.637
PROFICIENCIA_MT0.2330.2330.2160.2040.2120.1940.2430.2270.2970.2330.2540.2540.1340.1340.0960.0880.0430.0110.0110.0180.018-0.006-0.006-0.0060.0640.0890.066-0.0140.0890.5230.0641.0000.0621.0000.0661.0000.2440.0120.0110.0110.0180.0180.0280.0270.6370.6371.0001.000
PROFICIENCIA_MT_SAEB0.2330.2330.2160.2040.2120.1940.2430.2270.2970.2330.2540.2540.1340.1340.0960.0880.0430.0110.0110.0180.018-0.006-0.006-0.0060.0640.0890.066-0.0140.0890.5230.0641.0000.0621.0000.0661.0000.2440.0120.0110.0110.0180.0180.0280.0270.6370.6371.0001.000

Missing values

2025-05-20T09:27:05.402891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-20T09:27:05.711081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-05-20T09:27:05.889053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ID_SAEBID_REGIAOID_UFID_MUNICIPIOID_AREAID_ESCOLAIN_PUBLICAID_LOCALIZACAOID_TURMAID_SERIEID_ALUNOIN_SITUACAO_CENSOIN_PREENCHIMENTO_LPIN_PREENCHIMENTO_MTIN_PRESENCA_LPIN_PRESENCA_MTID_CADERNO_LPID_BLOCO_1_LPID_BLOCO_2_LPNU_BLOCO_1_ABERTA_LPNU_BLOCO_2_ABERTA_LPID_CADERNO_MTID_BLOCO_1_MTID_BLOCO_2_MTNU_BLOCO_1_ABERTA_MTNU_BLOCO_2_ABERTA_MTTX_RESP_BLOCO1_LPTX_RESP_BLOCO2_LPCO_CONCEITO_Q1_LPCO_CONCEITO_Q2_LPCO_RESPOSTA_TEXTOCO_CONCEITO_SEQUENCIACO_CONCEITO_COESAOCO_CONCEITO_PONTUACAOCO_CONCEITO_SEGMENTACAOCO_TEXTO_GRAFIATX_RESP_BLOCO1_MTTX_RESP_BLOCO2_MTCO_CONCEITO_Q1_MTCO_CONCEITO_Q2_MTIN_PROFICIENCIA_LPIN_PROFICIENCIA_MTIN_AMOSTRAESTRATOPESO_ALUNO_LPIN_ALFABETIZADOPROFICIENCIA_LPERRO_PADRAO_LPPROFICIENCIA_LP_SAEBERRO_PADRAO_LP_SAEBPESO_ALUNO_MTPROFICIENCIA_MTERRO_PADRAO_MTPROFICIENCIA_MT_SAEBERRO_PADRAO_MT_SAEB
02023111632217126147022212157050224939247211111206262206262DCBBBBBADDBDACBBAATXCCCCBABBDCCDCAACDBDCDBC2111111113225.6047080-0.4349240.258385724.12534514.3161375.411765-0.1060410.378020742.64626721.369347
12023111632217126147022212157050224939247311111206262206262DACBBCBCCDACCBAAAATXCBCBAABDBACBCBAAACDCDCC2120111113225.6047080-0.6005280.255719714.94983914.1684455.411765-0.0461040.359592746.03449920.327619
22023111632217126147022212157050224939253911111206262206262DCDBDDCAADDDDDADEENL*****ACBCDDCBBDDDCDCDCC110111113225.6047080-1.3967490.288573670.83413315.9887795.411765-0.8323990.450994701.58548025.494540
32023111632217126147022212157050224939254011111206262206262CCACBADCDDDDCCBBG.TXAABBAABBDACCBAAADCDCDBC2120111113225.6047080-0.4792950.356979721.66686719.7789055.4117651.2258740.463574817.93899226.205674
42023111632217126147022212157050224939254111111206262206262DCACBADCDDCCDCABAATXAACAAABBDACCBAAADCDCDAC2120111113225.60470811.8939350.556415853.15886730.8288875.4117651.7557320.581447847.89167432.868980
52023111632217126147022212157050224939254211111206262206262DCACBADCDDBCDCABBATXBBCBAABBDACDBAAADCBCDCC2111111113225.60470810.5331270.338114777.76146618.7336795.4117650.4171330.394988772.22111022.328556
62023111632217126147022212157050224939254311111206262206262ACAAAAACCDDCCBCABBTXCBCCBABBDACCBBAADCDCDCC210111113225.6047080-0.9204240.266251697.22552614.7519955.4117650.6925470.421521787.79020923.828461
72023111632217126147022212157050224939254411111206262206262BBCCABCCDDBCDCBBAATXAACBBABBDACDBBACDCDCDAC2120111113225.6047080-0.2453900.273538734.62670015.1557115.4117650.7216380.444310789.43470125.116673
82023111632217126147022212157050224939254511111206262206262DCBCBABCDDDDDCABAATXABBBAABBDACCBBAADCDCDBC210111113225.60470810.3764210.314957769.07895017.4505955.4117650.6925470.421521787.79020923.828461
92023111632217126147022212157050224939254610000206262206262..................BR.........................00111322NaN0NaNNaNNaNNaNNaNNaNNaNNaNNaN
ID_SAEBID_REGIAOID_UFID_MUNICIPIOID_AREAID_ESCOLAIN_PUBLICAID_LOCALIZACAOID_TURMAID_SERIEID_ALUNOIN_SITUACAO_CENSOIN_PREENCHIMENTO_LPIN_PREENCHIMENTO_MTIN_PRESENCA_LPIN_PRESENCA_MTID_CADERNO_LPID_BLOCO_1_LPID_BLOCO_2_LPNU_BLOCO_1_ABERTA_LPNU_BLOCO_2_ABERTA_LPID_CADERNO_MTID_BLOCO_1_MTID_BLOCO_2_MTNU_BLOCO_1_ABERTA_MTNU_BLOCO_2_ABERTA_MTTX_RESP_BLOCO1_LPTX_RESP_BLOCO2_LPCO_CONCEITO_Q1_LPCO_CONCEITO_Q2_LPCO_RESPOSTA_TEXTOCO_CONCEITO_SEQUENCIACO_CONCEITO_COESAOCO_CONCEITO_PONTUACAOCO_CONCEITO_SEGMENTACAOCO_TEXTO_GRAFIATX_RESP_BLOCO1_MTTX_RESP_BLOCO2_MTCO_CONCEITO_Q1_MTCO_CONCEITO_Q2_MTIN_PROFICIENCIA_LPIN_PROFICIENCIA_MTIN_AMOSTRAESTRATOPESO_ALUNO_LPIN_ALFABETIZADOPROFICIENCIA_LPERRO_PADRAO_LPPROFICIENCIA_LP_SAEBERRO_PADRAO_LP_SAEBPESO_ALUNO_MTPROFICIENCIA_MTERRO_PADRAO_MTPROFICIENCIA_MT_SAEBERRO_PADRAO_MT_SAEB
3709420235536327738161470207011498908256381160111113343433434ADCCCDCCDBBCCAADBATXBABAAADDADCCACCADBDAACA0201115341147.81982510.0444060.277612750.68324915.38147348.4308490.1393680.465720756.51918726.326996
3709520235536327738161470207011498908256381161111113343433434ADCBBDBCDCBCDAADAATXBACAADDDBDDCACDADBDCCCA21201115341147.81982510.9932250.402899803.25380122.32316248.4308491.6721520.481539843.16695727.221245
3709620235536327738161470207011498908256381162111113343433434ADCDCDBCDCBCDAADAATXBABAADDDBDDCACCADBDCBCA21201115341147.81982510.9991710.402480803.58324522.29992148.4308491.4635570.454880831.37516325.714224
3709720235536327738161470207011498908256400783111113343433434ADCBACADDCCBBCACABTXBACAACDDADDCACCADBBABCB10201115341147.8198250-0.5709170.227752716.59043112.61891748.4308490.1019840.397124754.40586722.449273
3709820235536327738161470207011498908256400784111113343433434ADCBCDBCDCBCDAADAATXBABAACDDBDDCACCCDCDCCCA12201115341147.81982511.1807740.438259813.64521324.28234448.4308490.5020320.417544777.02042423.603631
3709920235536327738161470207011498908256400785111113343433434ADCBCDBCDDBCDAADG.TXBACAADDDBCDCACCADBDCBCA12201115341147.8198251-0.0339070.357652746.34420319.81617348.4308490.9269340.423720801.04003923.952776
3710020235536327738161470207011498908256400786110103343433434ADBABDDDBCADBDCBABBR.........................1015341147.8198250-1.1325680.293099685.47141516.239540NaNNaNNaNNaNNaN
3710120235536327738161470207011498908256400787111113343433434ADCBADBCDCBCDAADAABR.....CDBBDBCACCADBDCDCA12201115341147.81982510.6378590.403976783.56429922.38281848.4308490.5556860.414705780.05347523.443118
3710220235536327738161470207011498908256400788111113343433434ADCCCDBCDCBDDAADAATXBACAACDDBDDCAACADBDBBCA0201115341147.81982510.6972710.353281786.85607119.57401248.4308490.3027710.465971765.75629526.341175
3710320235536327738161470207011498908256400789100003343433434..................BR.........................00153411NaN0NaNNaNNaNNaNNaNNaNNaNNaNNaN